Image retargeting aims to adjust the aspect ratio of images to accommodate various display devices. While existing methods consider both foreground semantics and background inpainting, their Seam-carving-based framework is inherently destructive, often compromising the structural integrity of foreground instances. Furthermore, conventional inpainting models struggle to achieve pixel-level accuracy with global-only guidance, leading to local inconsistencies and background distortions. To address these challenges, we reformulate image retargeting as a instance-level re-layout task. By Adaptive Instance Relocation and Dual-guidance Repainting (AIR-DR), our method preserves the structural integrity of the foreground and recovers the background with consistent details. Additionally, we introduce an adaptive retargeting decision that maintains robustness across challenging retargeting scenarios and any ratios. Extensive experiments on multiple public datasets across various aspect ratios demonstrate that our approach consistently outperforms existing methods in both objective metrics and subjective evaluations. Comprehensive ablation studies further validate the effectiveness of each component.

In the RGB-D vision community, extensive research has been focused on designing multi-modal learning strategies and fusion structures. However, the complementary and fusion mechanisms in RGB-D models remain a opaque box. In this paper, we present an analytical framework and a novel score to dissect the RGB-D vision community. Our approach involves measuring proposed semantic variance and feature similarity across modalities and levels, conducting visual and quantitative analyzes on multi-modal learning through comprehensive experiments. Specifically, we investigate the consistency and specialty of features across modalities, evolution rules within each modality, and the collaboration logic used when optimizing a RGB-D model. Our studies reveal/verify several important findings, such as the discrepancy in cross-modal features and the hybrid multi-modal cooperation rule, which highlights consistency and specialty simultaneously for complementary inference. We also showcase the versatility of the proposed RGB-D dissection method and introduce a straightforward fusion strategy based on our findings, which delivers significant enhancements across various tasks and even other multi-modal data.

Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-temporal-resolution motion cues and excel in challenging scenarios. These complementary characteristics underscore the potential of integrating frame and event data for optical flow estimation. However, most cross-modal approaches fail to fully utilize the complementary advantages, relying instead on simply stacking information. This study introduces a novel approach that uses a spatially dense modality to guide the aggregation of the temporally dense event modality, achieving effective cross-modal fusion. Specifically, we propose an event-enhanced frame representation that preserves the rich texture of frames and the basic structure of events. We use the enhanced representation as the guiding modality and employ events to capture temporally dense motion information. The robust motion features derived from the guiding modality direct the aggregation of motion information from events. To further enhance fusion, we propose a transformer-based module that complements sparse event motion features with spatially rich frame information and enhances global information propagation. Additionally, a mix-fusion encoder is designed to extract comprehensive spatiotemporal contextual features from both modalities. Extensive experiments on the MVSEC and DSEC-Flow datasets demonstrate the effectiveness of our framework. Leveraging the complementary strengths of frames and events, our method achieves leading performance on the DSEC-Flow dataset. Compared to the event-only model, frame guidance improves accuracy by 10%. Furthermore, it outperforms the state-of-the-art fusion-based method with a 4% accuracy gain and a 45% reduction in inference time. The code is publicly available athttps://github.com/ZhouQianang/STFlow.

The general-purpose Segment Anything Model (SAM) is limited by the inherent constraints of RGB sensors, which render it inadequate for challenging real-world scenarios such as adverse lighting conditions and rapid motion. In contrast, event cameras, a novel type of bio-inspired visual sensor, offer distinct imaging advantages, including high temporal resolution and a high dynamic range. The event streams generated by these cameras provide spatiotemporal dynamic cues that are often absent in conventional image frames. To overcome the limitations of RGB-based models, we propose SAM with Event-based Assistance (EvSAM) , a novel RGB-event multi-modal semantic segmentation framework. EvSAM leverages the strong generalization capabilities of SAM while incorporating the complementary characteristics of event data to enhance scene comprehension, particularly under adverse conditions. To address the challenges of fusing two modals (image and event) with large data format discrepancy, we introduce two core components: the Multi-spatiotemporal-scale Patch Alignment Block (MS 2 PAB) and the Event-based Feature Injector (EFInj) for SAM. Specifically, the MS \({}^{2}\) PAB captures spatiotemporal semantic coherence from the event stream and transforms it into a frame-based complementary representation using a multi-spatiotemporal alignment strategy. The EFInj introduces a dynamic event feature update mechanism, wherein the fused features at a given layer guide the adaptive generation of deeper event representations. This process facilitates the integration of RGB spatial semantics with event-based motion cues. Owing to these core designs, EvSAM demonstrates superior performance on event-based semantic segmentation datasets, thereby fully validating its distinct advantages in handling extreme visual scenarios. Furthermore, we extend our model to the task of depth estimation, which further demonstrates its strong generalization ability and scalability for various downstream applications.

When dealing with multi-view data, the heterogeneity of data attributes across different views often leads to label ambiguity. To effectively address this challenge, this paper designs a Multi-View Partial-Label Learning (MVPLL) framework, where each training instance is described by multiple view features and associated with a set of candidate labels, among which only one is correct. The key to deal with such problem lies in how to effectively fuse multi-view information and accurately disambiguate these ambiguous labels. In this paper, we propose a novel approach named CFDM, which explores the consistency and complementarity of multi-view data by multi-view contrastive fusion and reduces label ambiguity by multi-class contrastive prototype disambiguation. Specifically, we first extract view-specific representations using multiple view-specific autoencoders, and then integrate multi-view information through both inter-view and intra-view contrastive fusion to enhance the distinctiveness of these representations. Afterwards, we utilize these distinctive representations to establish and update prototype vectors for each class within each view. Based on these, we apply contrastive prototype disambiguation to learn global class prototypes and accordingly reduce label ambiguity. In our model, multi-view contrastive fusion and multi-class contrastive prototype disambiguation are conducted mutually to enhance each other within a coherent framework, leading to a more ideal classification performance. Experimental results on multiple datasets have demonstrated that our proposed method is superior to other state-of-the-art methods.

Event cameras have gained attention in segmentation due to their higher temporal resolution and dynamic range compared to traditional cameras. However, they struggle with issues like lack of color perception and triggering only at motion edges, making it hard to distinguish objects with similar contours or segment spatially continuous objects. Our work aims to address these often overlooked issues. Based on the assumption that various objects exhibit different motion patterns, we believe that embedding the historical motion states of objects into segmented scenes can effectively address these challenges. Inspired by this, we propose the ESS framework ``Know Where You Are From" (KWYAF), which incorporates past motion cues through spatio-temporal propagation embedding. This framework features two core components: the Sequential Motion Encoding Module (SME) and the Event-Based Reliable Region Selection Mechanism (ER²SM). SMEs construct prior motion features through spatio-temporal correlation modeling for boosting final segmentation, while ER²SM adapts to identify high-confidence regions, embedding motion more precisely through local window masks and reliable region selection. A large number of experiments have demonstrated the effectiveness of our proposed framework in terms of both quantity and quality.

Federated Multi-View Clustering (FMVC) aims to learn a global clustering model from heterogeneous data distributed across different devices, where each device only stores one view of all clustering samples. The key to deal with such problem lies in how to effectively fuse these heterogeneous samples while strictly preserve the data privacy across multiple devices. In this paper, we propose a novel structural graph learning framework named MGCD, which leverages both consistency and diversity of multi-view graph structure across global view-fusion server and local view-specific clients to achieve desired clustering while better preserves data privacy. Specifically, in each local client, we design a dual autoencoder to extract the latent consensuses and specificities of each view, where self-representation construction is introduced to generate the corresponding view-specific diversity graph. In the global server, the consistency implied in uploaded diversity graphs are further distilled and then incorporated into the consistency graph for subsequent cross-view contrastive fusion. During the training process, the server generates a global consistency graph and distributes it to each client for assisting in diversity graph construction, while the clients extract view-specific information and upload it to the server for more reliable consistency graph generation. The ``server-client'' interaction is conducted in an iterative manner, where the consistency implied in each local client is gradually aggregated into the global consistency graph, and the final clustering results are obtained by spectral clustering on the desired global consistency graph. Extensive experiments on various datasets have demonstrated the effectiveness of our proposed method on clustering federated multi-view data.

Salient object detection (SOD) methods for 2D images have great significance in the field of human-computer interaction (HCI). However, as a common data format in HCI, the SOD research in the form of 3D point cloud data remains limited. Previous works commonly treat this task as point cloud segmentation, which perceives all points in the scene for prediction. However, these methods neglect that SOD is designed to simulate human visual perception where human can only see the surfaces rather than occluded point clouds. Thereby, these methods may fail when meet such situations. This paper aims to solve this problem by approximately simulating the perception paradigm of humans towards 3D scenes. Thus, we propose a framework based on the 3D visual point cloud backbone and its multi-view projection named MSV-PCT. Specifically, instead of relying solely on general point cloud learning frameworks, we additionally introduce multi-sparse-view learning branches to supplement the SOD perception. Furthermore, we propose a novel point cloud edge detection loss function to effectively address artifacts, enabling the accurate segmentation of the edges of salient objects from the background. Finally, to evaluate the generalization of point cloud SOD methods, we introduce a new approach to generate simulated PC-SOD datasets from RGBD-SOD data. Experiments on the simulated datasets show that MSV-PCT achieves better accuracy and robustness.

In multi-view multi-label classification (MVML), each object is described by several heterogeneous views while annotated with multiple related labels. The key to learn from such complicate data lies in how to fuse cross-view features and explore multi-label correlations, while accordingly obtain correct assignments between each object and its corresponding labels. In this paper, we proposed an advanced MVML method named VAMS, which treats each object as a bag of views and reformulates the task of MVML as a “view-label” matching selection problem. Specifically, we first construct an object graph and a label graph respectively. In the object graph, nodes represent the multi-view representation of an object, and each view node is connected to its K-nearest neighbor within its own view. In the label graph, nodes represent the semantic representation of a label. Then, we connect each view node with all labels to generate the unified “view-label” matching graph. Afterwards, a graph network block is introduced to aggregate and update all nodes and edges on the matching graph, and further generating a structural representation that fuses multi-view heterogeneity and multi-label correlations for each view and label. Finally, we derive a prediction score for each view-label matching and select the optimal matching via optimizing a weighted cross-entropy loss. Extensive results on various datasets have verified that our proposed VAMS can achieve superior or comparable performance against state-of-the-art methods.

Event-based semantic segmentation (ESS) has attracted researchers' attention recently, as event cameras can solve problems such as under/over-exposure or motion blur that are difficult for RGB cameras to handle. However, event data are noisy and sparse, resulting in difficulties for the model to locate and extract reliable cues from their sparse representations, especially when performing pixel-level tasks. In this paper, we propose a novel framework ESEG to alleviate the dilemma. Given that event signals relate closely to moving edges, instead of proposing complex structures to expect them to recognize those reliable edge regions behind event signals on their own, we introduce the explicit edge-semantic supervision as a reference to let the ESS model globally optimize semantics, considering the high confidence of event data in edge regions. In addition, we propose a fusion module named Density-Aware Dynamic-Window Cross Attention Fusion (D\textsuperscript{2}CAF), in which the density perception, cross-attention, and dynamic window masking mechanisms are jointly imposed to optimize edge-dense feature fusion, leveraging the characteristics of event cameras. Experimental results on DSEC and DDD17 datasets demonstrate the efficacy of the ESEG framework and its core designs.

Multi-view classification aims at unifying the data from multiple views to complementarily enhance the classification performance. Unfortunately, two major problems in multi-view data are damaging model performance. The first is feature heterogeneity, which makes it hard to fuse features from different views. Considering this, we introduce a multi-scale alignment module, including an instance-scale alignment module and a prototype-scale alignment module to mine the commonality from an inter-view perspective and an inter-class perspective respectively, jointly alleviating feature heterogeneity. The second is information redundancy which easily incurs ambiguous data to blur class boundaries and impair model generalization. Therefore, we propose a novel expanded boundary by extending the original class boundary with fuzzy set theory, which adaptively adjusts the boundary to fit ambiguous data. By integrating the expanded boundary into the prototype-scale alignment module, our model further tightens the produced representations and reduces boundary ambiguity. Additionally, compared with the original class boundary, the expanded boundary preserves more margins for classifying unseen data, which guarantees the model generalization. Extensive experiment results across various real-world datasets demonstrate the superiority of the proposed model against existing state-of-the-art methods.

Different modalities hold considerable gaps in optimization trajectories, including speeds and paths, which lead to *modality laziness* and *modality clash* when jointly training multimodal models, resulting in insufficient and imbalanced multimodal learning. Existing methods focus on enforcing the weak modality by adding modality-specific optimization objectives, aligning their optimization speeds, or decomposing multimodal learning to enhance unimodal learning. These methods fail to achieve both unimodal sufficiency and multimodal balance. In this paper, we, for the first time, address both concerns by proposing multimodal Data Remixing, including decoupling multimodal data and filtering hard samples for each modality to mitigate modality imbalance; and then batch-level reassembling to align the gradient directions and avoid cross-modal interference, thus enhancing unimodal learning sufficiency. Experimental results demonstrate that our method can be seamlessly integrated with existing approaches, improving accuracy by approximately **6.50\%$\uparrow$** on CREMAD and **3.41\%$\uparrow$** on Kinetic-Sounds, without training set expansion or additional computational overhead during inference. The source code is available at Data Remixing.

Event cameras, with their capacity to provide high temporal resolution information between frames, are increasingly utilized for video frame interpolation (VFI) in challenging scenarios characterized by high-speed motion and significant occlusion. However, prevalent issues of blur and distortion within the keyframes and ground truth data used for training and inference in these demanding conditions are frequently overlooked. This oversight impedes the perceptual realism and multi-scene generalization capabilities of existing event-based VFI (E-VFI) methods when generating interpolated frames. Motivated by the observation that semantic-perceptual discrepancies between degraded and pristine images are considerably smaller than their image-level differences, we introduce EPA. This novel E-VFI framework diverges from approaches reliant on direct image-level supervision by constructing multilevel, degradation-insensitive semantic perceptual supervisory signals to enhance the perceptual realism and multi-scene generalization of the model's predictions. Specifically, EPA operates in two phases: it first employs a DINO-based perceptual extractor, a customized style adapter, and a reconstruction generator to derive multi-layered, degradation-insensitive semantic-perceptual features ($\mathcal{S}$). Second, a novel Bidirectional Event-Guided Alignment (BEGA) module utilizes deformable convolutions to align perceptual features from keyframes to ground truth with inter-frame temporal guidance extracted from event signals. By decoupling the learning process from direct image-level supervision, EPA enhances model robustness against degraded keyframes and unreliable ground truth information. Extensive experiments demonstrate that this approach yields interpolated frames more consistent with human perceptual preferences. *The code will be released upon acceptance.*

Multi-view multi-label classification is a crucial machine learning paradigm aimed at building robust multi-label predictors by integrating heterogeneous features from various sources while addressing multiple correlated labels. However, in real-world applications, concerns over data confidentiality and security often prevent data exchange or fusion across different sources, leading to the challenging issue of data islands. To tackle this problem, we propose a general federated multi-view multi-label classification method, FMVML, which integrates a novel multi-view multi-label classification technique into a federated learning framework. This approach enables cross-view feature fusion and multi-label semantic classification while preserving the data privacy of each independent source. Within this federated framework, we first extract view-specific information from each individual client to capture unique characteristics and then consolidate consensus information from different views on the global server to represent shared features. Unlike previous methods, our approach enhances cross-view fusion and semantic expression by jointly capturing both feature and semantic aspects of specificity and commonality. The final label predictions are generated by combining the view-specific predictions from individual clients and the consensus predictions from the global server. Extensive experiments across various applications demonstrate that FMVML fully leverages multi-view data in a privacy-preserving manner and consistently outperforms state-of-the-art methods.

Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird observation and protection but also a prevalent task for image classification in multimedia processing and computer vision. However, FBIC suffers from several challenges, such as bird molting, complex background, and arbitrary bird posture. To effectively tackle these challenges, we present a novel invariant cues-aware feature concentration Transformer (TransIFC), which learns invariant and core information in bird images. To this end, two novel modules are proposed to leverage the characteristics of bird images, namely, the hierarchy stage feature aggregation (HSFA) module and the feature in feature abstraction (FFA) module. The HSFA module aggregates the multiscale information of bird images by concatenating multilayer features. The FFA module extracts the invariant cues of birds through feature selection based on discrimination scores. Transformer is employed as the backbone to reveal the long-dependent semantic relationships in bird images. Moreover, abundant visualizations are provided to prove the interpretability of the HSFA and FFA modules in TransIFC. Comprehensive experiments demonstrate that TransIFC can achieve state-of-the-art performance on the CUB-200-2011 dataset (91.0%) and the NABirds dataset (90.9%). Finally, extended experiments have been conducted on the Stanford Cars dataset to suggest the potential of generalizing our method on other fine-grained visual classification tasks.

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches learning sparse point-based representations through graph CNNs (GCN) become more popular. Yet, these graph techniques hold lower performance than their frame-based counterpart due to two issues: (i) Biased graph structures that don't properly incorporate varied attributes (such as semantics, and spatial and temporal signals) for each vertex, resulting in inaccurate graph representations. (ii) A shortage of robust pretrained models. Here we solve the first problem by proposing a new event-based GCN (EDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To address the second problem, we introduce a novel learning framework called cross-representation distillation (CRD), which leverages the dense representation of events as a cross-representation auxiliary to provide additional supervision and prior knowledge for the event graph. This frame-to-graph distillation allows us to benefit from the large-scale priors provided by CNNs while still retaining the advantages of graph-based models. Extensive experiments show our model and learning framework are effective and generalize well across multiple vision tasks.

Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the ad-vancement of VFI. Thanks to the high temporal resolution benefits, event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm, which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However, this reliance engenders a heavy dependency on the quality and consis-tency of keyframes, rendering these methods susceptible to challenges in extreme real-world scenarios, such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference, obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inher-ently noisy nature of event data, we prioritize the relia-bility of the event-based reference, leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides, we implement a bi-directional event-guided alignment from keyframes to the reference using the introduced E-PCD module. Finally, a transformer-based decoder is adopted for prediction re-finement. Comprehensive experimental evaluations on both synthetic and real-world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.

Semantic segmentation, a fundamental visual task ubiquitously employed in sectors ranging from transportation and robotics to healthcare, has always captivated the research community. In the wake of rapid advancements in large model research, the foundation model for semantic segmentation tasks, termed the Segment Anything Model (SAM), has been introduced. This model substantially addresses the dilemma of poor generalizability of previous segmentation models and the disadvantage in requiring to retrain the whole model on variant datasets. Nonetheless, segmentation models developed on SAM remain constrained by the inherent limitations of RGB sensors, particularly in scenarios characterized by complex lighting conditions and high-speed motion. Motivated by these observations, a natural recourse is to adapt SAM to additional visual modalities without compromising its robust generalizability. To achieve this, we introduce a lightweight SAM-Event-Adapter (SE-Adapter) module, which incorporates event camera data into a cross-modal learning architecture based on SAM, with only limited tunable parameters incremental. Capitalizing on the high dynamic range and temporal resolution afforded by event cameras, our proposed multi-modal Event-RGB learning architecture effectively augments the performance of semantic segmentation tasks. In addition, we propose a novel paradigm for representing event data in a patch format compatible with transformer-based models, employing multi-spatiotemporal scale encoding to efficiently extract motion and semantic correlations from event representations. Exhaustive empirical evaluations conducted on the DSEC-Semantic and DDD17 datasets provide validation of the effectiveness and rationality of our proposed approach.

Video salient object ranking aims to simulate the human attention mechanism by dynamically prioritizing the visual attraction of objects in a scene over time. Despite its numerous practical applications, this area remains underexplored. In this work, we propose a graph model for video salient object ranking. This graph simultaneously explores multi-scale spatial contrasts and intra-/inter-instance temporal correlations across frames to extract diverse spatio-temporal saliency cues. It has two advantages: 1. Unlike previous methods that only perform global inter-frame contrast or compare all proposals across frames globally, we explicitly model the motion of each instance by comparing its features with those in the same spatial region in adjacent frames, thus obtaining more accurate motion saliency cues. 2. We synchronize the spatio-temporal saliency cues in a single graph for joint optimization, which exhibits better dynamics compared to the previous stage-wise methods that prioritize spatial cues followed by temporal cues. Additionally, we propose a simple yet effective video retargeting method based on video saliency ranking. Extensive experiments demonstrate the superiority of our model in video salient object ranking and the effectiveness of the video retargeting method. Our codes/models are released at [https://github.com/zyf-815/VSOR/tree/main](https://github.com/zyf-815/VSOR/tree/main).

Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often struggle to balance the preservation of key semantics and image quality, resulting in either deformation or loss of important objects, or the introduction of local artifacts such as discontinuous pixels and inconsistent regenerated content. To address these issues, we propose a content-aware retargeting method called PruneRepaint. It incorporates semantic importance for each pixel to guide the identification of regions that need to be pruned or preserved in order to maintain key semantics. Additionally, we introduce an adaptive repainting module that selects image regions for repainting based on the distribution of pruned pixels and the proportion between foreground size and target aspect ratio, thus achieving local smoothness after pruning. By focusing on the content and structure of the foreground, our PruneRepaint approach adaptively avoids key content loss and deformation, while effectively mitigating artifacts with local repainting. We conduct experiments on the public RetargetMe benchmark and demonstrate through objective experimental results and subjective user studies that our method outperforms previous approaches in terms of preserving semantics and aesthetics, as well as better generalization across diverse aspect ratios. Codes will be available at https://github.com/fhshen2022/PruneRepaint.

Event cameras are neuromorphic vision sensors that record a scene as sparse and asynchronous event streams. Most event-based methods project events into dense frames and process them using conventional vision models, resulting in high computational complexity. A recent trend is to develop point-based networks that achieve efficient event processing by learning sparse representations. However, existing works may lack robust local information aggregators and effective feature interaction operations, thus limiting their modeling capabilities. To this end, we propose an attention-aware model named Event Voxel Set Transformer (EVSTr) for efficient spatiotemporal representation learning on event streams. It first converts the event stream into voxel sets and then hierarchically aggregates voxel features to obtain robust representations. The core of EVSTr is an event voxel transformer encoder that consists of two well-designed components, including the Multi-Scale Neighbor Embedding Layer (MNEL) for local information aggregation and the Voxel Self-Attention Layer (VSAL) for global feature interaction. Enabling the network to incorporate a long-range temporal structure, we introduce a segment modeling strategy (S2TM) to learn motion patterns from a sequence of segmented voxel sets. The proposed model is evaluated on two recognition tasks, including object classification and action recognition. To provide a convincing model evaluation, we present a new event-based action recognition dataset (NeuroHAR) recorded in challenging scenarios. Comprehensive experiments show that EVSTr achieves state-of-the-art performance while maintaining low model complexity.

Previous multi-modal transformers for RGB-D salient object detection (SOD) generally directly connect all patches from two modalities to model cross-modal correlation and perform multi-modal combination without differentiation, which can lead to confusing and inefficient fusion. Instead, we disentangle the cross-modal complementarity from two views to reduce cross-modal fusion ambiguity: 1) Context disentanglement. We argue that modeling long-range dependencies across modalities as done before is uninformative due to the severe modality gap. Differently, we propose to disentangle the cross-modal complementary contexts to intra-modal self-attention to explore global complementary understanding, and spatial-aligned inter-modal attention to capture local cross-modal correlations, respectively. 2) Representation disentanglement. Unlike previous undifferentiated combination of cross-modal representations, we find that cross-modal cues complement each other by enhancing common discriminative regions and mutually supplement modal-specific highlights. On top of this, we divide the tokens into consistent and private ones in the channel dimension to disentangle the multi-modal integration path and explicitly boost two complementary ways. By progressively propagate this strategy across layers, the proposed Disentangled Feature Pyramid module (DFP) enables informative cross-modal cross-level integration and better fusion adaptivity. Comprehensive experiments on a large variety of public datasets verify the efficacy of our context and representation disentanglement and the consistent improvement over state-of-the-art models. Additionally, our cross-modal attention hierarchy can be plug-and-play for different backbone architectures (both transformer and CNN) and downstream tasks, and experiments on a CNN-based model and RGB-D semantic segmentation verify this generalization ability.

Bio-inspired event cameras record a scene as sparse and asynchronous “events” by detecting per-pixel brightness changes. Such cameras show great potential in challenging scene understanding tasks, benefiting from the imaging advantages of high dynamic range and high temporal resolution. Considering the complementarity between event and standard cameras, we propose a multi-modal fusion network (EISNet) to improve the semantic segmentation performance. The key challenges of this topic lie in (i) how to encode event data to represent accurate scene information and (ii) how to fuse multi-modal complementary features by considering the characteristics of two modalities. To solve the first challenge, we propose an Activity-Aware Event Integration Module (AEIM) to convert event data into frame-based representations with high-confidence details via scene activity modeling. To tackle the second challenge, we introduce the Modality Recalibration and Fusion Module (MRFM) to recalibrate modal-specific representations and then aggregate multi-modal features at multiple stages. MRFM learns to generate modal-oriented masks to guide the merging of complementary features, achieving adaptive fusion. Based on these two core designs, our proposed EISNet adopts an encoder-decoder transformer architecture for accurate semantic segmentation using events and images. Experimental results show that our model outperforms state-of-the-art methods by a large margin on event-based semantic segmentation datasets. The code is publicly available athttps://github.com/bochenxie/EISNet.

Head pose estimation (HPE) has been widely used in the fields of human machine interaction, self-driving, and attention estimation. However, existing methods cannot deal with extreme head pose randomness and serious occlusions. To address these challenges, we identify three cues from head images, namely, neighborhood similarities, significant facial changes, and critical minority relationships. To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned. Specifically, we design several orientation tokens to explicitly encode the basic orientation regions. Meanwhile, a novel token guide multiloss function is designed to guide the orientation tokens as they learn the desired regional similarities and relationships. We evaluate the proposed method on three challenging benchmark HPE datasets. Experiments show that our method achieves better performance compared with state-of-the-art methods. Our code is publicly available at https://github.com/zc2023/TokenHPE.

Head pose estimation (HPE) is an indispensable upstream task in the fields of human-machine interaction, self-driving, and attention detection. However, practical head pose applications suffer from several challenges, such as severe occlusion, low illumination, and extreme orientations. To address these challenges, we identify three cues from head images, namely, critical minority relationships, neighborhood orientation relationships, and significant facial changes. On the basis of the three cues, two key insights on head poses are revealed: 1) intra-orientation relationship and 2) cross-orientation relationship. To leverage two key insights above, a novel relationship-driven method is proposed based on the Transformer architecture, in which facial and orientation relationships can be learned. Specifically, we design several orientation tokens to explicitly encode basic orientation regions. Besides, a novel token guide multi-loss function is accordingly designed to guide the orientation tokens as they learn the desired regional similarities and relationships. Experimental results on three challenging benchmark HPE datasets show that our proposed TokenHPE achieves state-of-the-art performance. Moreover, qualitative visualizations are provided to verify the effectiveness of the token-learning methodology.

Event cameras attract researchers' attention due to their low power consumption, high dynamic range, and extremely high temporal resolution. Learning models on event-based object classification have recently achieved massive success by accumulating sparse events into dense frames to apply traditional 2D learning methods. Yet, these approaches necessitate heavy-weight models and are with high computational complexity due to the redundant information introduced by the sparse-to-dense conversion, limiting the potential of event cameras on real-life applications. This study aims to address the core problem of balancing accuracy and model complexity for event-based classification models. To this end, we introduce a novel graph representation for event data to exploit their sparsity better and customize a lightweight voxel graph convolutional neural network (EV-VGCNN) for event-based classification. Specifically, (1) using voxel-wise vertices rather than previous point-wise inputs to explicitly exploit regional 2D semantics of event streams while keeping the sparsity; (2) proposing a multi-scale feature relational layer (MFRL) to extract spatial and motion cues from each vertex discriminatively concerning its distances to neighbors. Comprehensive experiments show that our model can advance state-of-the-art classification accuracy with extremely low model complexity (merely 0.84M parameters).

Event-based object recognition has drawn increasing attention for event cameras’ distinguished advantages of low power consumption and high dynamic range. For this new modality, previous works based on customizing low-level descriptors are vulnerable to noise and with limited generalizability. Although recent works turn to design various deep neural networks to extract event features, they either suffer from data insufficiency to fully train the event-based model or fail to encode spatial and temporal cues simultaneously with their single view network. In this work, we address these limitations by proposing a multi-view attention-aware network, in which an event stream is projected to multi-view 2D maps to utilize well-trained 2D models and explore spatio-temporal complements. Besides, the attention mechanism is used to boost the complements in different streams for better joint inference. Comprehensive experiments show the large superiority of our model over state-of-the-art methods as well as the efficacy of our multi-view fusion framework for event data.

Event cameras have recently drawn massive attention in the computer vision community because of their low power consumption and high response speed. These cameras produce sparse and non-uniform spatiotemporal representations of a scene. These characteristics of representations make it difficult for event-based models to extract discriminative cues (such as textures and geometric relationships). Consequently, event-based methods usually perform poorly compared to their conventional image counterparts. Considering that traditional images and event signals share considerable visual information, this paper aims to improve the feature extraction ability of event-based models by using knowledge distilled from the image domain to additionally provide explicit feature-level supervision for the learning of event data. Specifically, we propose a simple yet effective distillation learning framework, including multi-level customized knowledge distillation constraints. Our framework can significantly boost the feature extraction process for event data and is applicable to various downstream tasks. We evaluate our framework on high-level and low-level tasks, i.e., object classification and optical flow prediction. Experimental results show that our framework can effectively improve the performance of event-based models on both tasks by a large margin. Furthermore, we present a 10K dataset (CEP-DVS) for event-based object classification. This dataset consists of samples recorded under random motion trajectories that can better evaluate the motion robustness of the event-based model and is compatible with multi-modality vision tasks.

Depth is beneficial for salient object detection (SOD) for its additional saliency cues. Existing RGBD SOD methods focus on tailoring complicated cross-modal fusion topologies, which although achieve encouraging performance, are with a high risk of over-fitting and ambiguous in studying cross-modal complementarity. Different from these conventional approaches combining cross-modal features entirely without differentiating, we concentrate our attention on decoupling the diverse cross-modal complements to simplify the fusion process and enhance the fusion sufficiency. We argue that if cross-modal heterogeneous representations can be disentangled explicitly, the cross-modal fusion process can hold less uncertainty, while enjoying better adaptability. To this end, we design a disentangled cross-modal fusion network to expose structural and content representations from both modalities by cross-modal reconstruction. For different scenes, the disentangled representations allow the fusion module to easily identify, and incorporate desired complements for informative multi-modal fusion. Extensive experiments show the effectiveness of our designs and a large outperformance over state-of-the-art methods.