This paper introduces MultiBooth, a method that generates images from texts containing various concepts from users.Despite diffusion models bringing significant advancements for customized text-to-image generation, existing methods often struggle with multi-concept scenarios due to low concept fidelity and high inference cost. MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase. During the single-concept learning phase, we employ a multi-modal image encoder and an efficient concept encoding technique to learn a concise and discriminative representation for each concept. In the multi-concept integration phase, we use bounding boxes to define the generation area for each concept within the cross-attention map. This method enables the creation of individual concepts within their specified regions, thereby facilitating the formation of multi-concept images. This strategy not only improves concept fidelity but also reduces additional inference cost. MultiBooth surpasses various baselines in both qualitative and quantitative evaluations, showcasing its superior performance and computational efficiency.

Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing methods still face the challenges of $\textit{\textbf{inconsistency}}$ and $\textit{\textbf{inefficiency}}$. They struggle to maintain consistency in both the foreground and background during concept swapping, especially when the shape difference is large between objects. Additionally, they either require time-consuming training processes or involve redundant calculations during inference. To tackle these issues, we introduce InstantSwap, a new CCS method that aims to handle sharp shape disparity at speed. Specifically, we first extract the bbox of the object in the source image $\textit{automatically}$ based on attention map analysis and leverage the bbox to achieve both foreground and background consistency. For background consistency, we remove the gradient outside the bbox during the swapping process so that the background is free from being modified. For foreground consistency, we employ a cross-attention mechanism to inject semantic information into both source and target concepts inside the box. This helps learn semantic-enhanced representations that encourage the swapping process to focus on the foreground objects. To improve swapping speed, we avoid computing gradients at each timestep but instead calculate them periodically to reduce the number of forward passes, which improves efficiency a lot with a little sacrifice on performance. Finally, we establish a benchmark dataset to facilitate comprehensive evaluation. Extensive evaluations demonstrate the superiority and versatility of InstantSwap.

Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion-based models (DM) have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. To tackle these problems, we propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Specifically, Reti-Diff comprises two significant components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RGformer). RLDM is designed to acquire Retinex knowledge, extracting reflectance and illumination priors to facilitate detailed reconstruction and illumination correction. RGformer subsequently utilizes these compact priors to guide the decomposition of image features into their respective reflectance and illumination components. Following this, RGformer further enhances and consolidates these decomposed features, resulting in the production of refined images with consistent content and robustness to handle complex degradation scenarios. Extensive experiments demonstrate that Reti-Diff outperforms existing methods on three IDIR tasks, as well as downstream applications.

Concealed object segmentation (COS) is a challenging problem that focuses on identifying objects that are visually blended into their background. Existing methods often employ reversible strategies to concentrate on uncertain regions but only focus on the mask level, overlooking the valuable of the RGB domain. To address this, we propose a Reversible Unfolding Network (RUN) in this paper. RUN formulates the COS task as a foreground-background separation process and incorporates an extra residual sparsity constraint to minimize segmentation uncertainties. The optimization solution of the proposed model is unfolded into a multistage network, allowing the original fixed parameters to become learnable. Each stage of RUN consists of two reversible modules: the Segmentation-Oriented Foreground Separation (SOFS) module and the Reconstruction-Oriented Background Extraction (ROBE) module. SOFS applies the reversible strategy at the mask level and introduces Reversible State Space to capture non-local information. ROBE extends this to the RGB domain, employing a reconstruction network to address conflicting foreground and background regions identified as distortion-prone areas, which arise from their separate estimation by independent modules. As the stages progress, RUN gradually facilitates reversible modeling of foreground and background in both the mask and RGB domains, reducing false-positive and false-negative regions. Extensive experiments demonstrate the superior performance of RUN and underscore the promise of unfolding-based frameworks for COS and other high-level vision tasks. Code is available at https://github.com/ChunmingHe/RUN.

Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos from a fixed set of action categories. However, this constrained setting is not viable in an open world where test videos inevitably come beyond the trained action categories. In this paper, we address the practical yet challenging Open-Vocabulary Action Detection (OVAD) problem. It aims to detect any action in test videos while training a model on a fixed set of action categories. To achieve such an open-vocabulary capability, we propose a novel method OpenMixer that exploits the inherent semantics and localizability of large vision-language models (VLM) within the family of query-based detection transformers (DETR). Specifically, the OpenMixer is developed by spatial and temporal OpertMixer blocks (S-OMB and T-OMB), and a dynamically fused alignment (DFA) module. The three components collectively enjoy the merits of strong generalization from pretrained VLMs and end-to-end learning from DETR design. Moreover, we established OVAD benchmarks under various settings, and the experimental results show that the OpenMixer performs the best over baselines for detecting seen and unseen actions. We release the codes, models, and dataset splits at https://github.com/Cogito2012/0penMixer.

Incompletely-Supervised Concealed Object Segmentation (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments, utilizing incompletely annotated data, such as weak and semi-annotations, for model training. This task remains highly challenging due to (1) the limited supervision provided by the incompletely annotated training data, and (2) the difficulty of distinguishing concealed objects from the background, which arises from the intrinsic similarities in concealed scenarios. In this paper, we introduce the first unified method for ISCOS to address these challenges. To tackle the issue of incomplete supervision, we propose a unified mean-teacher framework, SEE, that leverages the vision foundation model, "Segment Anything Model (SAM)", to generate pseudo-labels using coarse masks produced by the teacher model as prompts. To mitigate the effect of low-quality segmentation masks, we introduce a series of strategies for pseudo-label generation, storage, and supervision. These strategies aim to produce informative pseudo-labels, store the best pseudo-labels generated, and select the most reliable components to guide the student model, thereby ensuring robust network training. Additionally, to tackle the issue of intrinsic similarity, we design a hybrid-granularity feature grouping module that groups features at different granularities and aggregates these results. By clustering similar features, this module promotes segmentation coherence, facilitating more complete segmentation for both single-object and multiple-object images. We validate the effectiveness of our approach across multiple ISCOS tasks, and experimental results demonstrate that our method achieves state-of-the-art performance. Furthermore, SEE can serve as a plug-and-play solution, enhancing the performance of existing models.

The latest versatile video coding (VVC) standard proposed by the Joint Video Exploration Team (JVET) has significantly improved coding efficiency compared to that of its predecessor, while introducing an extremely higher computational complexity by $6\sim 26$ times. The quad-tree plus multi-type tree (QTMT)-based coding unit (CU) partition accounts for most of the encoding time in VVC encoding. This paper proposes a data-driven fast CU partition approach based on an efficient Transformer model to accelerate VVC inter-coding. First, we establish a large-scale database for inter-mode VVC, comprising diverse CU partition patterns from more than 800 raw video sequences across various resolutions and contents. Next, we propose a deep neural network model with a Transformer-based temporal topology for predicting the CU partition, named as TCP-Net, which is adaptive to the group of pictures (GOP) hierarchy in VVC. Then, we design a two-stage structured output for TCP-Net, reflecting both the locations of CU edges and the split modes of all possible CUs. Accordingly, we develop a dual-supervised optimization mechanism to train the TCP-Net model with improved accuracy. The experimental results have verified that our approach can reduce the encoding time by $46.89\sim 55.91$ % with negligible rate-distortion (RD) degradation, outperforming other state-of-the-art approaches.

Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance.

Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To address these challenges, we first introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes, effectively integrating physical knowledge into deep networks to restore haze-contaminated details. Additionally, we propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator, to generate high-quality pseudo labels for network training. Specifically, we provide an optimal label pool to store the best pseudo-labels during network training, leveraging both global and local coherence to select high-quality candidates and assign weights to prioritize haze-free regions. We verify the effectiveness of our method, with experiments demonstrating that it achieves state-of-the-art performance on RID tasks. Code will be available at https://github.com/cnyvfang/CORUN-Colabator.

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this article, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multiencoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets verify that our method outperforms existing techniques in terms of both enhancement quality and downstream task performance. The code and the newly collected datasets are publicly available at https://github.com/ChunmingHe/HQG-Net.

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich source domain to an unlabeled target domain without seeing source data. While most existing SFOD methods generate pseudo labels via a source-pretrained model to guide training, these pseudo labels usually contain high noises due to heavy domain discrepancy. In order to obtain better pseudo supervisions, we divide the target domain into source-similar and source-dissimilar parts and align them in the feature space by adversarial learning.Specifically, we design a detection variance-based criterion to divide the target domain. This criterion is motivated by a finding that larger detection variances denote higher recall and larger similarity to the source domain. Then we incorporate an adversarial module into a mean teacher framework to drive the feature spaces of these two subsets indistinguishable. Extensive experiments on multiple cross-domain object detection datasets demonstrate that our proposed method consistently outperforms the compared SFOD methods. Our implementation is available at https://github.com/ChuQiaosong.

Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data. It is derived from unsupervised domain adaptation but has the advantage of not requiring labeled source data to learn adaptive models. This makes it particularly useful in real-world applications where access to source data is restricted. While there has been some SFDA work for images, little attention has been paid to videos. Naively extending image-based methods to videos without considering the unique properties of videos often leads to unsatisfactory results. In this paper, we propose a simple and highly flexible method for Source-Free Video Domain Adaptation (SFVDA), which extensively exploits consistency learning for videos from spatial, temporal, and historical perspectives. Our method is based on the assumption that videos of the same action category are drawn from the same low-dimensional space, regardless of the spatio-temporal variations in the high-dimensional space that cause domain shifts. To overcome domain shifts, we simulate spatio-temporal variations by applying spatial and temporal augmentations on target videos and encourage the model to make consistent predictions from a video and its augmented versions. Due to the simple design, our method can be applied to various SFVDA settings, and experiments show that our method achieves state-of-the-art performance for all the settings.

Camouflaged object detection (COD) aims to address the tough issue of identifying camouflaged objects visually blended into the surrounding backgrounds. COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries. Existing approaches to this problem have developed various techniques to mimic the human visual system. Albeit effective in many cases, these methods still struggle when camouflaged objects are so deceptive to the vision system. In this paper, we propose the FEature Decomposition and Edge Reconstruction (FEDER) model for COD. The FEDER model addresses the intrinsic similarity of foreground and background by decomposing the features into different frequency bands using learnable wavelets. It then focuses on the most informative bands to mine subtle cues that differentiate foreground and background. To achieve this, a frequency attention module and a guidance-based feature aggregation module are developed. To combat the ambiguous boundary problem, we propose to learn an auxiliary edge reconstruction task alongside the COD task. We design an ordinary differential equation-inspired edge reconstruction module that generates exact edges. By learning the auxiliary task in conjunction with the COD task, the FEDER model can generate precise prediction maps with accurate object boundaries. Experiments show that our FEDER model significantly outperforms state-of-the-art methods with cheaper computational and memory costs. The code will be available at https://github.com/ChunmingHe/FEDER.

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given image and condition. In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image. Compared to previous direct-synthesis-based works, our proposed LFDM can better synthesize spatial details and temporal motion by fully utilizing the spatial content of the given image and warping it in the latent space according to the generated temporally-coherent flow. The training of LFDM consists of two separate stages: (1) an unsupervised learning stage to train a latent flow auto-encoder for spatial content generation, including a flow predictor to estimate latent flow between pairs of video frames, and (2) a conditional learning stage to train a 3D-UNet-based diffusion model (DM) for temporal latent flow generation. Unlike previous DMs operating in pixel space or latent feature space that couples spatial and temporal information, the DM in our LFDM only needs to learn a low-dimensional latent flow space for motion generation, thus being more computationally efficient. We conduct comprehensive experiments on multiple datasets, where LFDM consistently outperforms prior arts. Furthermore, we show that LFDM can be easily adapted to new domains by simply finetuning the image decoder. Our code is available at https://github.com/nihaomiao/CVPR23_LFDM.

Semi-Supervised Domain Adaptation (SSDA) is a recently emerging research topic that extends from the widely-investigated Unsupervised Domain Adaptation (UDA) by further having a few target samples labeled, i.e., the model is trained with labeled source samples, unlabeled target samples as well as a few labeled target samples. Compared with UDA, the key to SSDA lies how to most effectively utilize the few labeled target samples. Existing SSDA approaches simply merge the few precious labeled target samples into vast labeled source samples or further align them, which dilutes the value of labeled target samples and thus still obtains a biased model. To remedy this, in this paper, we propose to decouple SSDA as an UDA problem and a semi-supervised learning problem where we first learn an UDA model using labeled source and unlabeled target samples and then adapt the learned UDA model in a semi-supervised way using labeled and unlabeled target samples. By utilizing the labeled source samples and target samples separately, the bias problem can be well mitigated. We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples. Experiments show our approach outperforms existing methods.

Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging complementary information from images captured by different sensors. Among these algorithms, deep unfolding network (DUN)-based methods achieve promising performance but still suffer from two issues: they lack a degradation-resistant-oriented fusion model and struggle to adequately consider the structural properties of DUNs, making them vulnerable to degradation scenarios. In this paper, we propose a Degradation-Resistant Unfolding Network (DeRUN) for the HIF task to generate high-quality fused images even in degradation scenarios. Specifically, we introduce a novel HIF model for degradation resistance and derive its optimization procedures. Then, we incorporate the optimization unfolding process into the proposed DeRUN for end-to-end training. To ensure the robustness and efficiency of DeRUN, we employ a joint constraint strategy and a lightweight partial weight sharing module. To train DeRUN, we further propose a gradient direction-based entropy loss with powerful texture representation capacity. Extensive experiments show that DeRUN significantly outperforms existing methods on four HIF tasks, as well as downstream applications, with cheaper computational and memory costs.

Federated learning casts a light on the collaboration of distributed local clients with privacy protected to attain a more generic global model. However, significant distribution shift in input/label space across different clients makes it challenging to well generalize to all clients, which motivates personalized federated learning (PFL). Existing PFL methods typically customize the local model by fine-tuning with limited local supervision and the global model regularizer, which secures local specificity but risks ruining the global discriminative knowledge. In this paper, we propose a novel Personalized Semantics Excitation (PSE) mechanism to breakthrough this limitation by exciting and fusing personalized semantics from the global model during local model customization. Specifically, PSE explores channel-wise gradient differentiation across global and local models to identify important low-level semantics mostly from convolutional layers which are embedded into the client-specific training. In addition, PSE deploys the collaboration of global and local models to enrich high-level feature representations and facilitate the robustness of client classifier through a cross-model attention module. Extensive experiments and analysis on various image classification benchmarks demonstrate the effectiveness and advantage of our method over the state-of-the-art PFL methods.

Recent few-shot video classification (FSVC) works achieve promising performance by capturing similarity across support and query samples with different temporal alignment strategies or learning discriminative features via Transformer block within each episode. However, they ignore two important issues: a) It is difficult to capture rich intrinsic action semantics from a limited number of support instances within each task. b) Redundant or irrelevant frames in videos easily weaken the positive influence of discriminative frames. To address these two issues, this paper proposes a novel Representation Fusion and Promotion Learning (RFPL) mechanism with two sub-modules: meta-action learning (MAL) and reinforced image representation (RIR). Concretely, during training stage, we perform online learning for seeking a task-shared meta-action bank to enrich task-specific action representation by injecting global knowledge. Besides, we exploit reinforcement learning to obtain the importance of each frame and refine the representation. This operation maximizes the contribution of discriminative frames to further capture the similarity of support and query samples from the same category. Our RFPL framework is highly flexible that it can be integrated with many existing FSVC methods. Extensive experiments show that RFPL significantly enhances the performance of existing FSVC models when integrated with them.

Recently, increasing interest has been drawn in Transformer-based models for No-reference Image Quality Assessment (NR-IQA), especially for the hybrid approach. The hybrid approach tend to apply Transformer to aggregate quality information from feature maps extracted by Convolutional Neural Networks (CNN). However, existing methods cannot fully utilize the information of hierarchical features extracted by the deep neural network, resulting in the limited performance of image quality evaluation. In this work, we propose a novel Hierarchical Feature Fusion Transformer for NR-IQA (HiFFTiq), which is able to effectively exploit complementary strengths of features extracted by different layers. Further, we propose a new Uniform Partition Pooling (UPP) which can reduce the resolution of input features via uniform partitions and can well retain the quality-related information compared to the traditional pooling method Sliding Window Pooling (SWP). The results of experiment demonstrate that HiFFTiq leads to improvements of performance over the state-of-the-art methods on three large scale NR-IQA datasets.

Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment objects well blended with surrounding environments using sparsely-annotated data for model training. It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning. In this paper, we propose a new WSCOS method to address these two challenges. To tackle the intrinsic similarity challenge, we design a multi-scale feature grouping module that first groups features at different granularities and then aggregates these grouping results. By grouping similar features together, it encourages segmentation coherence, helping obtain complete segmentation results for both single and multiple-object images. For the weak supervision challenge, we utilize the recently-proposed vision foundation model, ``Segment Anything Model (SAM)'', and use the provided sparse annotations as prompts to generate segmentation masks, which are used to train the model. To alleviate the impact of low-quality segmentation masks, we further propose a series of strategies, including multi-augmentation result ensemble, entropy-based pixel-level weighting, and entropy-based image-level selection. These strategies help provide more reliable supervision to train the segmentation model. We verify the effectiveness of our method on various WSCOS tasks, and experiments demonstrate that our method achieves state-of-the-art performance on these tasks.

MOTIVATION: We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides. RESULTS: Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences. AVAILABILITY AND IMPLEMENTATION: The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

As a bridge between language and vision domains, cross-modal retrieval between images and texts is a hot research topic in recent years. It remains challenging because the current image representations usually lack semantic concepts in the corresponding sentence captions. To address this issue, we introduce an intuitive and interpretable model to learn a common embedding space for alignments between images and text descriptions. Specifically, our model first incorporates the semantic relationship information into visual and textual features by performing region or word relationship reasoning. Then it utilizes the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced features, select the discriminative information and gradually grow representations for the whole scene. Through the alignment learning, the learned visual representations capture key objects and semantic concepts of a scene as in the corresponding text caption. Experiments on MS-COCO [1] and Flickr30K [2] datasets validate that our method surpasses many recent state-of-the-arts with a clear margin. In addition to the effectiveness, our methods are also very efficient at the inference stage. Thanks to the effective overall representation learning with visual semantic reasoning, our methods can already achieve very strong performance by only relying on the simple inner-product to obtain similarity scores between images and captions. Experiments validate the proposed methods are more than 30-75 times faster than many recent methods with code public available. Instead of following the recent trend of using complex local matching strategies [3], [4], [5], [6] to pursue good performance while sacrificing efficiency, we show that the simple global matching strategy can still be very effective, efficient and achieve even better performance based on our framework.

Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lacking compositionality could have severe implications for robustness and fairness, e.g., inability to synthesize the face images of underrepresented demographic groups. In this paper, we introduce a new framework, StyleT2I, to improve the compositionality of text-to-image synthesis. Specifically, we propose a CLIP-guided Contrastive Loss to better distinguish different compositions among different sentences. To further improve the compositionality, we design a novel Semantic Matching Loss and a Spatial Constraint to identify attributes' latent directions for intended spatial region manipulations, leading to better disentangled latent representations of attributes. Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis. In addition, we leverage the$l_{2}$-norm regularization of identified latent directions (norm penalty) to strike a nice balance between image-text alignment and image fidelity. In the experiments, we devise a new dataset split and an evaluation metric to evaluate the compositionality of text-to-image synthesis models. The results show that StyleT2I outperforms previous approaches in terms of the consistency between the input text and synthesized images and achieves higher fidelity.

Anomaly Detection (AD) aims to find defective patterns or abnormal samples among data, and has been a hot research topic due to various real-world applications. While various AD methods have been proposed, most of them assume the availability of a clean (anomaly-free) training set, which however may be hard to guarantee in many real-world industry applications. This motivates us to investigate Unsupervised Anomaly Detection (UAD) in which the training set includes both normal and abnormal samples. In this paper, we address the UAD problem by proposing a Self-Training and Knowledge Distillation (STKD) model. STKD combats anomalies in the training set by iteratively alternating between excluding samples of high anomaly probabilities and training the model with the purified training set. Despite that the model is trained with a cleaner training set, the inevitably existing anomalies may still cause negative impact. STKD alleviates this by regularizing the model to respond similarly to a teacher model which has not been trained with noisy data. Experiments show that STKD consistently produces more robust performance with different levels of anomalies.

Multi-view outlier detection has a wide range of applications and has been well investigated in recent years. However, 1) most existing state-of-the-art methods cannot efficiently handle outlier detection problem for large-scale multi-view data, since exploring pairwise constraints among different views causes highly-computational cost; 2) the data collected from original heterogeneous feature spaces further increases the consistent difficulty of multi-view outlier detection. To address these issues, we present a fast multi-view outlier detection model via learning a low-rank latent subspace representation with deep encoder architecture, which can not only efficiently identify the outliers for large-scale data even with numerous data views, but also exploit a discriminative common latent subspace shared by all the views. First, we learn a set of orthogonal bases as view-specific dictionaries from a small dataset, which is randomly sampled from the original dataset. Benefitting from view-specific dictionaries, the sampled data is projected and decomposed as a shared and discriminative latent subspace representations, which correspond to the view-consistent and view-specific components across multiple views, respectively. Then, the obtained discriminative latent representations are applied to train the view-specific deep encoders, which can efficiently compute the abnormal score for the remaining instances. Our proposed model can cost-effectively identify the outliers in large-scale datasets from numerous data views with less computational complexity. Experiments conducted on eight real datasets and a synthesis dataset show that our proposed model outperforms the existing ones on effectiveness and efficiency.

Multiview subspace clustering (MVSC) leverages the complementary information among different views of multiview data and seeks a consensus subspace clustering result better than that using any individual view. Though proved effective in some cases, existing MVSC methods often obtain unsatisfactory results since they perform subspace analysis with raw features that are often of high dimensions and contain noises. To remedy this, we propose a self-guided deep multiview subspace clustering (SDMSC) model that performs joint deep feature embedding and subspace analysis. SDMSC comprehensively explores multiview data and strives to obtain a consensus data affinity relationship agreed by features from not only all views but also all intermediate embedding spaces. With more constraints being cast, the desirable data affinity relationship is supposed to be more reliably recovered. Besides, to secure effective deep feature embedding without label supervision, we propose to use the data affinity relationship obtained with raw features as the supervision signals to self-guide the embedding process. With this strategy, the risk that our deep clustering model being trapped in bad local minima is reduced, bringing us satisfactory clustering results in a higher possibility. The experiments on seven widely used datasets show the proposed method significantly outperforms the state-of-the-art clustering methods. Our code is available at https://github.com/kailigo/dmvsc.git.

One of the key tasks for an intelligent visual surveillance system is to automatically re-identify objects of interest, e.g., persons or vehicles, from nonoverlapping camera views. This demand incurs the vast investigation of person re-identification (re-ID) and vehicle re-ID techniques, especially those deep learning-based ones. While most recent algorithms focus on designing new convolutional neural networks, less attention is paid to the loss functions, which are of vital roles as well. Triplet loss and softmax loss are the two losses that are extensively used, both of which, however, have limitations. Triplet loss optimizes the model to produce features with which samples from the same class have higher similarity than those from different classes. The problem of triplet loss is that the number of triplets to be constructed grows cubically with training samples, which causes scalability issue, unstable performance, and slow convergence. Softmax loss has favorable scalable property and is widely used for large-scale classification problems. However, since Softmax loss only aims to separate well training classes, its performance for re-ID tasks is not desirable because the model is tested to measure the similarity of samples from unseen classes. We propose the support neighbor (SN) loss, which avoids the limitations of the abovementioned two losses. Unlike triplet loss that is calculated based on triplets, SN loss is derived from K -nearest neighbors (SNs) of anchor samples. The SNs of an anchor are unique, containing more valuable contextual information and neighborhood structure of the anchor, and thus contribute to more stable performance and reliable embedding from image space to feature space. Based on the SNs, a softmax-like separation term and a squeeze term are proposed, which encourage interclass separation and intraclass compactness, respectively. Experiments show that SN loss surpasses triplet and softmax losses with the same backbone network and reaches the state-of-the-art performance for both person and vehicle re-ID using a ResNet50 backbone when combined with training tricks.

Searching for relative mobile user interface (UI) design examples can aid interface designers in gaining inspiration and comparing design alternatives. However, finding such design examples is challenging, especially as current search systems rely on only text-based queries and do not consider the UI structure and content into account. This paper introduces VINS, a visual search framework, that takes as input a UI image (wireframe, high-fidelity) and retrieves visually similar design examples. We first survey interface designers to better understand their example finding process. We then develop a large-scale UI dataset that provides an accurate specification of the interface’s view hierarchy (i.e., all the UI components and their specific location). By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure. The framework achieves a mean Average Precision of 76.39% for the UI detection and high performance in querying similar UI designs.

High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed information for reliable diagnosis and quantitative image analyses. Deep convolutional neural networks (CNNs) have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. The LR MR images usually share some visual characteristics: repeating patterns, relatively simpler structures, and less informative background. Most previous CNN-based SR methods treat the spatial pixels (including the background) equally. They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors. Such global descriptors enhance the network’s ability to focus on more informative regions and structures in MR images. We further build relationship among those global descriptors and propose primitive relationship reasoning attention. The global descriptors are further refined with learned attention. To fully make use of the aggregated information, we adaptively recalibrate feature responses with learned adaptive attention vectors. These attention vectors select a subset of global descriptors to complement each spatial location for accurate details and texture reconstruction. We propose squeeze and excitation attention with residual scaling, which not only stabilizes the training but also makes it flexible to other basic networks. Extensive experiments show the effectiveness of our proposed SERAN, which clearly surpasses state-of-the-art methods on benchmarks quantitatively and visually.

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain. Several SSDA methods have been proposed recently, which however fail to fully exploit the value of the few labeled target samples. In this paper, we propose Enhanced Categorical Alignment and Consistency Learning (ECACL), a holistic SSDA framework that incorporates multiple mutually complementary domain alignment techniques. ECACL includes two categorical domain alignment techniques that achieve class-level alignment, a strong data augmentation based technique that enhances the model’s generalizability and a consistency learning based technique that forces the model to be robust with image perturbations. These techniques are applied on one or multiple of the three inputs (labeled source, unlabeled target, and labeled target) and align the domains from different perspectives. ECACL unifies them together and achieves fairly comprehensive domain alignments that are much better than the existing methods: For example, ECACL raises the state-of-the-art accuracy from 68.4 to 81.1 on VisDA2017 and from 45.5 to 53.4 on DomainNet for the 1-shot setting. Our code is available at https://github.com/kailigo/pacl.

Despite recent progress on semantic segmentation, there still exist huge challenges in high or ultra-high resolution images semantic segmentation. Although the latest collaborative global-local semantic segmentation methods such as GLNet [4] and PPN [18] have achieved impressive results, they are inefficient and not fit for practical applications. Thus, in this paper, we propose a novel and efficient collaborative global-local framework on the basis of PPN named Faster-PPN for high or ultra-high resolution images semantic segmentation which makes a better trade-off between the efficient and effectiveness towards the real-time speed. Specially, we propose Dual Mutual Learning to improve the feature representation of global and local branches, which conducts knowledge distillation mutually between the global and local branches. Furthermore, we design the Pixel Proposal Fusion Module to conduct the fine-grained selection mechanism which further reduces the redundant pixels for fusion resulting in the improvement of inference speed. The experimental results on three challenging high or ultra-high resolution datasets DeepGlobe, ISIC and BACH demonstrate that Faster-PPN achieves the best performance on accuracy, inference speed and memory usage compared with state-of-the-art approaches. Especially, our method achieves real-time and near real-time speed with 36 FPS and 17.7 FPS on ISIC and DeepGlobe, respectively.

Domain generalization aims to learn a model that generalizes to unseen target domains from multiple source domains. Various approaches have been proposed to address this problem by adversarial learning, meta-learning, and data augmentation. However, those methods have no guarantee for target domain generalization. Motivated by an observation that the class-irrelevant information of sample in the form of semantic variation would lead to negative transfer, we propose to linearly disentangle the variation out of sample in feature space and impose a novel class decorrelation regularization on the feature variation. By doing so, the model would focus on the high-level categorical concept for model prediction while ignoring the misleading clue from other variations (including domain changes). As a result, we achieve state-of-the-art performances over all of widely used domain generalization benchmarks, namely PACS, VLCS, Office-Home, and Digits-DG with large margins. Further analysis reveals our method could learn a better domain-invariant representation, and decorrelated feature variation could successfully capture semantic meaning.

Image denoising is a classical topic yet still a challenging problem, especially for reducing noise from the texture information. Feature scaling (e.g., downscale and upscale) is a widely practice in image denoising to enlarge receptive field size and save resources. However, such a common operation would lose some visual informative details. To address those problems, we propose fast and accurate image denoising via attention guided scaling (AGS). We find that the main informative feature channel and visual primitives during the scaling should keep similar. We then propose to extract the global channel-wise attention to maintain main channel information. Moreover, we propose to collect global descriptors by considering the entire spatial feature. And we then distribute the global descriptors to local positions of the scaled feature, based on their specific needs. We further introduce AGS for adversarial training, resulting in a more powerful discriminator. Extensive experiments show the effectiveness of our proposed method, where we clearly surpass all the state-of-the-art methods on most popular synthetic and real-world denoising benchmarks quantitatively and visually. We further show that our network contributes to other high-level vision applications and improves their performances significantly.

The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL), which aims to learn concept of new classes with a few labeled samples. A natural approach to FSL is data augmentation and many recent works have proved the feasibility by proposing various data synthesis models. However, these models fail to well secure the discriminability and diversity of the synthesized data and thus often produce undesirable results. In this paper, we propose Adversarial Feature Hallucination Networks (AFHN) which is based on conditional Wasserstein Generative Adversarial networks (cWGAN) and hallucinates diverse and discriminative features conditioned on the few labeled samples. Two novel regularizers, i.e., the classification regularizer and the anti-collapse regularizer, are incorporated into AFHN to encourage discriminability and diversity of the synthesized features, respectively. Ablation study verifies the effectiveness of the proposed cWGAN based feature hallucination framework and the proposed regularizers. Comparative results on three common benchmark datasets substantiate the superiority of AFHN to existing data augmentation based FSL approaches and other state-of-the-art ones.

Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like intelligent document editing and understanding. DOD remains a challenging problem as document objects vary significantly in layout, size, aspect ratio, texture, etc. An additional challenge arises in practice because large labeled training datasets are only available for domains that differ from the target domain. We investigate cross-domain DOD, where the goal is to learn a detector for the target domain using labeled data from the source domain and only unlabeled data from the target domain. Documents from the two domains may vary significantly in layout, language, and genre. We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation. For each dataset, we provide the page images, bounding box annotations, PDF files, and the rendering layers extracted from the PDF files. Moreover, we propose a novel cross-domain DOD model which builds upon the standard detection model and addresses domain shifts by incorporating three novel alignment modules: Feature Pyramid Alignment (FPA) module, Region Alignment (RA) module and Rendering Layer alignment (RLA) module. Extensive experiments on the benchmark suite substantiate the efficacy of the three proposed modules and the proposed method significantly outperforms the baseline methods. The project page is at \url{https://github.com/kailigo/cddod}.

The rich and accessible labeled data fueled the revolutionary successes of deep learning in object recognition. However, recognizing objects of novel classes with limited supervision information provided, i.e., Novel Object Recognition (NOR), remains a challenging task. We identify in this paper two key factors for the success of NOR that previous approaches fail to simultaneously guarantee. The first is producing discriminative feature representations for images of novel classes, and the second is generating a flexible classifier readily adapted to novel classes provided with limited supervision signals. To secure both key factors, we propose a framework which decouples a deep classification model into a feature extraction module and a classification module. We learn the former to ensure feature discriminability with a standard multi-class classification task by fully utilizing the competing information among all classes within a training set, and learn the latter to secure adaptability by training a meta-learner network which generates classifier weights whenever provided with minimal supervision information of target classes. Extensive experiments on common benchmark datasets in the settings of both zero-shot and few-shot learning demonstrate our method achieves state-of-the-art performance.

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms usually formulate it as a semantic-visual correspondence problem, by learning mappings from one feature space to the other. Despite being reasonable, previous approaches essentially discard the highly precious discriminative power of visual features in an implicit way, and thus produce undesirable results. We instead reformulate ZSL as a conditioned visual classification problem, i.e., classifying visual features based on the classifiers learned from the semantic descriptions. With this reformulation, we develop algorithms targeting various ZSL settings: For the conventional setting, we propose to train a deep neural network that directly generates visual feature classifiers from the semantic attributes with an episode-based training scheme; For the generalized setting, we concatenate the learned highly discriminative classifiers for seen classes and the generated classifiers for unseen classes to classify visual features of all classes; For the transductive setting, we exploit unlabeled data to effectively calibrate the classifier generator using a novel learning-without-forgetting self-training mechanism and guide the process by a robust generalized cross-entropy loss. Extensive experiments show that our proposed algorithms significantly outperform state-of-the-art methods by large margins on most benchmark datasets in all the ZSL settings.

Image-text matching has been a hot research topic bridging the vision and language areas. It remains challenging because the current representation of image usually lacks global semantic concepts as in its corresponding text caption. To address this issue, we propose a simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene. Specifically, we first build up connections between image regions and perform reasoning with Graph Convolutional Networks to generate features with semantic relationships. Then, we propose to use the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced features, select the discriminative information and gradually generate the representation for the whole scene. Experiments validate that our method achieves a new state-of-the-art for the image-text matching on MS-COCO and Flickr30K datasets. It outperforms the current best method by 6.8% relatively for image retrieval and 4.8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set). On Flickr30K, our model improves image retrieval by 12.6% relatively and caption retrieval by 5.8% relatively (Recall@1).

The attention of a deep neural network obtained by back-propagating gradients can effectively explain the decision of the network. They can further be used to explicitly access to the network response to a specific pattern. Considering objects of the same category but from different domains share similar visual patterns, we propose to treat the network attention as a bridge to connect objects across domains. In this paper, we use knowledge from the source domain to guide the network's response to categories shared with the target domain. With weights sharing and domain adversary training, this knowledge can be successfully transferred by regularizing the network's response to the same category in the target domain. Specifically, we transfer the foreground prior from a simple single-label dataset to another complex multi-label dataset, leading to improvement of attention maps. Experiments about the weakly-supervised semantic segmentation task show the effectiveness of our method. Besides, we further explore and validate that the proposed method is able to improve the generalization ability of a classification network in domain adaptation and domain generalization settings.

Person reidentification (ReID) has recently been widely investigated for its vital role in surveillance and forensics applications. This paper addresses the low-resolution (LR) person ReID problem, which is of great practical meaning because pedestrians are often captured in LRs by surveillance cameras. Existing methods cope with this problem via some complicated and time-consuming strategies, making them less favorable, in practice, and meanwhile, their performances are far from satisfactory. Instead, we solve this problem by developing a discriminative semicoupled projective dictionary learning (DSPDL) model, which adopts the efficient projective dictionary learning strategy, and jointly learns a pair of dictionaries and a mapping function to model the correspondence of the cross-view data. A parameterless cross-view graph regularizer incorporating both positive and negative pair information is designed to enhance the discriminability of the dictionaries. Another weakness of existing approaches to this problem is that they are only applicable for the scenario where the cross-camera image sets have a globally uniform resolution gap. This fact undermines their practicality because the resolution gaps between cross-camera images often vary person by person in practice. To overcome this hurdle, we extend the proposed DSPDL model to the variational resolution gap scenario, basically by learning multiple pairs of dictionaries and multiple mapping functions. A novel technique is proposed to rerank and fuse the results obtained from all dictionary pairs. Experiments on five public data sets show the proposed method achieves superior performances to the state-of-the-art ones.

Identifying multi-view outliers is challenging because of the complex data distributions across different views. Existing methods cope this problem by exploiting pairwise constraints across different views to obtain new feature representations,based on which certain outlier score measurements are defined. Due to the use of pairwise constraint, it is complicated and time-consuming for existing methods to detect outliers from three or more views. In this paper, we propose a novel method capable of detecting outliers from any number of dataviews. Our method first learns latent discriminant representations for all view data and defines a novel outlier score function based on the latent discriminant representations. Specifically, we represent multi-view data by a global low-rank representation shared by all views and residual representations specific to each view. Through analyzing the view-specific residual representations of all views, we can get the outlier score for every sample. Moreover, we raise the problem of detectinga third type of multi-view outliers which are neglected by existing methods. Experiments on six datasets show our method outperforms the existing ones in identifying all types of multi-view outliers, often by large margins.

Person re-identification (re-ID) is a fundamental task in automated video surveillance. In real-world visual surveillance systems, a person is often captured in quite low resolutions. So we often need to perform low-resolution person re-ID, where images captured by different cameras have great resolution divergences. Existing methods cope problem via some complicated and time-consuming strategies, making them less favorable in practice, and their performances are far from satisfactory. In this paper, we design a novel Discriminative Semi-coupled Projective Dictionary Learning (DSPDL) model to effectively and efficiently solve this problem. Specifically, we propose to jointly learn a pair of dictionaries and a mapping to bridge the gap across low(er) and high(er) resolution person images. Besides, we develop a novel graph regularizer to incorporate positive and negative image pair information in a parameterless fashion. Meanwhile, we adopt the efficient and powerful projective dictionary learning technique to boost the our efficiency. Experiments on three public datasets show the superiority of the proposed method to the state-of-the-art ones.

Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus on designing new CNN architectures, while less attention is paid on investigating the loss functions. Verification loss and identification loss are two types of losses widely used to train various deep re-ID models, both of which however have limitations. Verification loss guides the networks to generate feature embeddings of which the intra-class variance is decreased while the inter-class ones is enlarged. However, training networks with verification loss tends to be of slow convergence and unstable performance when the number of training samples is large. On the other hand, identification loss has good separating and scalable property. But its neglect to explicitly reduce the intra-class variance limits its performance on re-ID, because the same person may have significant appearance disparity across different camera views. To avoid the limitations of the two types of losses, we propose a new loss, called support neighbor (SN) loss. Rather than being derived from data sample pairs or triplets, SN loss is calculated based on the positive and negative support neighbor sets of each anchor sample, which contain more valuable contextual information and neighborhood structure that are beneficial for more stable performance. To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets. To reduce intra-class variance, the distance between the anchor's nearest positive neighbor and furthest positive sample is penalized. Integrating SN loss on top of Resnet50, superior re-ID results to the state-of-the-art ones are obtained on several widely used datasets.