Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, achieving precise control over motion for interactive video asset generation remains challenging. To this end, we propose Image Conductor, a method for precise control of camera transitions and object movements to generate video assets from a single image. An well-cultivated training strategy is proposed to separate distinct camera and object motion by camera LoRA weights and object LoRA weights. To further eliminate motion ambiguity from ill-posed trajectories, we introduce a camera-free guidance technique during inference process, enhancing object movements while eliminating camera transitions. Additionally, we develop a trajectory-oriented video motion data curation pipeline for training. Quantitative and qualitative experiments demonstrate our method's precision and fine-grained control in generating motion-controllable videos from images, advancing the practical application of interactive video synthesis.

Text-based person retrieval (TPR) has gained significant attention as a fine-grained and challenging task that closely aligns with practical applications. Tailoring CLIP to person domain is now a emerging research topic due to the abundant knowledge of vision-language pretraining, but challenges still remain during fine-tuning: (i) Previous full-model fine-tuning in TPR is computationally expensive and prone to overfitting.(ii) Existing parameter-efficient transfer learning (PETL) for TPR lacks of fine-grained feature extraction. To address these issues, we propose Domain-Aware Mixture-of-Adapters (DM-Adapter), which unifies Mixture-of-Experts (MOE) and PETL to enhance fine-grained feature representations while maintaining efficiency. Specifically, Sparse Mixture-of-Adapters is designed in parallel to MLP layers in both vision and language branches, where different experts specialize in distinct aspects of person knowledge to handle features more finely. To promote the router to exploit domain information effectively and alleviate the routing imbalance, Domain-Aware Router is then developed by building a novel gating function and injecting learnable domain-aware prompts. Extensive experiments show that our DM-Adapter achieves state-of-the-art performance, outperforming previous methods by a significant margin.

Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multiview alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient overlap or occlusions between views. In this paper, we propose NVComposer, a novel approach that eliminates the need for explicit external alignment. NVComposer enables the generative model to implicitly infer spatial and geometric relationships between multiple conditional views by introducing two key components: 1) an image-pose dualstream diffusion model that simultaneously generates target novel views and condition camera poses, and 2) a geometry-aware feature alignment module that distills geometric priors from dense stereo models during training. Extensive experiments demonstrate that NVComposer achieves state-of-the-art performance in generative multi-view NVS tasks, removing the reliance on external alignment and thus improving model accessibility. Our approach shows substantial improvements in synthesis quality as the number of unposed input views increases, highlighting its potential for more flexible and accessible generative NVS systems.

Controllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such strict dense guidance impairs the quality of the generated video when the body shape of the reference character differs significantly from that of the driving video. In this paper, we present DisPose to mine more generalizable and effective control signals without additional dense input, which disentangles the sparse skeleton pose in human image animation into motion field guidance and keypoint correspondence. Specifically, we generate a dense motion field from a sparse motion field and the reference image, which provides region-level dense guidance while maintaining the generalization of the sparse pose control. We also extract diffusion features corresponding to pose keypoints from the reference image, and then these point features are transferred to the target pose to provide distinct identity information. To seamlessly integrate into existing models, we propose a plug-and-play hybrid ControlNet that improves the quality and consistency of generated videos while freezing the existing model parameters. Extensive qualitative and quantitative experiments demonstrate the superiority of DisPose compared to current methods. Project page: https://github.com/lihxxx/DisPose.

Meteorological heatmaps play a vital role in deciphering extreme weather phenomena, yet their inherent complexities-marked by irregular contours, unstructured patterns, and complex color variations-present unique analytical hurdles for state-of-the-art Vision-Language Models (VLMs). Current state-of-the-art models like GPT-4o, Qwen-VL, and LLaVA 1.6 struggle with tasks such as precise color identification and spatial localization, resulting in inaccurate or incomplete interpretations. To address these challenges, we introduce Sparse Position and Outline Tracking (SPOT), a novel algorithm specifically designed to process irregularly shaped colored regions in visual data. SPOT identifies and localizes these regions by extracting their spatial coordinates, enabling structured representations of irregular shapes. Building on SPOT, we construct ClimateIQA, a novel meteorological visual question answering (VQA) dataset, comprising 26,280 high-resolution heatmaps and 762,120 instruction samples for wind gust, total precipitation, wind chill index and heat index analysis. ClimateIQA enhances VLM training by incorporating spatial cues, geographic metadata, and reanalysis data, improving model accuracy in interpreting and describing extreme weather features. Furthermore, we develop Climate-Zoo, a suite of fine-tuned VLMs based on SPOT-empowered ClimateIQA, which significantly outperforms existing models in meteorological heatmap tasks.

As user expectations for image editing continue to rise, the demand for flexible, fine-grained manipulation of specific visual elements presents a challenge for current diffusion-based methods. In this work, we present BlobCtrl, a framework for element-level image editing based on a probabilistic blob-based representation. Treating blobs as visual primitives, BlobCtrl disentangles layout from appearance, affording fine-grained, controllable object-level elements manipulation. Our key contributions are twofold: 1) an in-context dual-branch diffusion model that separates foreground and background processing, incorporating blob representations to explicitly decouple layout and appearance; and 2) a self-supervised disentangle-then-reconstruct training paradigm with an identity-preserving loss function, along with tailored strategies to efficiently leverage blob-image pairs. To foster further research, we introduce BlobData for large-scale training, and BlobBench, a benchmark for systematic evaluation. Experimental results demonstrate that BlobCtrl achieves state-of-the-art performance in a variety of element-level editing tasks—such as object addition, removal, scaling, and replacement—while maintaining computational efficiency.

Text-based Person Retrieval (TPR) as a multi-modal task, which aims to retrieve the target person from a pool of candidate images given a text description, has recently garnered considerable attention due to the progress of contrastive visual-language pre-trained model. Prior works leverage pre-trained CLIP to extract person visual and textual features and fully fine-tune the entire network, which have shown notable performance improvements compared to uni-modal pre-training models. However, full-tuning a large model is prone to overfitting and hinders the generalization ability. In this paper, we propose a novelUnifiedParameter-Efficient Transfer Learning (PETL) method for Text-basedPersonRetrieval (UP-Person) to thoroughly transfer the multi-modal knowledge from CLIP. Specifically, UP-Person simultaneously integrates three lightweight PETL components including Prefix, LoRA and Adapter, where Prefix and LoRA are devised together to mine local information with task-specific information prompts, and Adapter is designed to adjust global feature representations. Additionally, two vanilla submodules are optimized to adapt to the unified architecture of TPR. For one thing, S-Prefix is proposed to boost attention of prefix and enhance the gradient propagation of prefix tokens, which improves the flexibility and performance of the vanilla prefix. For another thing, L-Adapter is designed in parallel with layer normalization to adjust the overall distribution, which can resolve conflicts caused by overlap and interaction among multiple submodules. Extensive experimental results demonstrate that our UP-Person achieves state-of-the-art results across various person retrieval datasets, including CUHK-PEDES, ICFG-PEDES and RSTPReid while merely fine-tuning 4.7% parameters. Code is available at https://github.com/Liu-Yating/UP-Person.

Multi-Intent spoken language understanding (SLU) can handle complicated utterances expressing multiple intents, which has attracted increasing attention from researchers. Although existing models have achieved promising performance, most of them still suffer from two leading problems: (1) each intent has its specific scope and the semantic information outside the scope might potentially hinder accurate predictions, i.e. scope barrier; (2) only the guidance from intent to slot is modeled but the guidance from slot to intent is often neglected, i.e. unidirectional guidance. In this paper, we propose a novel Multi-Intent SLU framework termed HAOT, which utilizes hierarchical attention to divide the scopes of each intent and applies optimal transport to achieve the mutual guidance between slot and intent. Experiments demonstrate that our model achieves state-of-the-art performance on two public Multi-Intent SLU datasets, obtaining the 3.4 improvement on MixATIS dataset compared to the previous best models in overall accuracy.

Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the sparsity dilemma in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. In this paper, we contend that exploiting easily available captions which describe general actions, i.e., auxiliary captions defined in our paper, will significantly boost the performance. To this end, we propose an Auxiliary Caption Network (ACNet) for video grounding. Specifically, we first introduce dense video captioning to generate dense captions and then obtain auxiliary captions by Non-Auxiliary Caption Suppression (NACS). To capture the potential information in auxiliary captions, we propose Caption Guided Attention (CGA) project the semantic relations between auxiliary captions and query sentences into temporal space and fuse them into visual representations. Considering the gap between auxiliary captions and ground truth, we propose Asymmetric Cross-modal Contrastive Learning (ACCL) for constructing more negative pairs to maximize cross-modal mutual information. Extensive experiments on three public datasets (i.e., ActivityNet Captions, TACoS and ActivityNet-CG) demonstrate that our method significantly outperforms state-of-the-art methods.

While vision-language pre-trained models (VL-PTMs) have advanced multimodal research in recent years, their mastery in a few languages like English restricts their applicability in broader communities. To this end, there is an increasing interest in developing multilingual VL models via a joint-learning setup, which, however, could be unrealistic due to expensive costs and data availability. In this work, we propose to extend VL-PTMs' language capacity by continual language learning (CLL), where a model needs to update its linguistic knowledge incrementally without suffering from catastrophic forgetting (CF). We begin our study by introducing a model dubbed CLL-CLIP, which builds upon CLIP, a prevailing VL-PTM that has acquired image-English text alignment. Specifically, CLL-CLIP contains an expandable token embedding layer to handle linguistic differences. It solely trains token embeddings to improve memory stability and is optimized under cross-modal and cross-lingual objectives to learn the alignment between images and multilingual texts. To alleviate CF raised by covariate shift and lexical overlap, we further propose a novel approach that ensures the identical distribution of all token embeddings during initialization and regularizes token embedding learning during training. We construct a CLL benchmark covering 36 languages based on MSCOCO and XM3600 datasets and then evaluate multilingual image-text retrieval performance. Extensive experiments verify the effectiveness of CLL-CLIP and show that our approach can boost CLL-CLIP, e.g., by 6.7% in text-to-image average Recall@1 on XM3600, and improve various state-of-the-art methods consistently. Our code and data are available at https://github.com/yangbang18/CLFM.

Multi-intent spoken language understanding (SLU) has garnered growing attention due to its ability to handle multiple intent utterances, which closely mirrors practical scenarios. Unlike traditional SLU, each intent in multi-intent SLU corresponds to its designated scope for slots, which occurs in certain fragments within the utterance. As a result, establishing precise scope alignment to mitigate noise impact emerges as a key challenge in multi-intent SLU. More seriously, they lack alignment between the predictions of the two sub-tasks due to task-independent decoding, resulting in a limitation on the overall performance. To address these challenges, we propose a novel framework termed Aligner² for multi-intent SLU, which contains an Adjustive Cross-task Aligner (ACA) and a Forced Cross-task Aligner (FCA). ACA utilizes the information conveyed by joint label embeddings to accurately align the scope of intent and corresponding slots, before the interaction of the two subtasks. FCA introduces reinforcement learning, to enforce the alignment of the task-specific hidden states after the interaction, which is explicitly guided by the prediction. Extensive experiments on two public multi-intent SLU datasets demonstrate the superiority of our Aligner² over state-of-the-art methods. More encouragingly, the proposed method Aligner² can be easily integrated into existing multi-intent SLU frameworks, to further boost performance.

Multimodal machine translation (MMT) aims to improve the performance of machine translation with the help of visual information, which has received widespread attention recently.It has been verified that visual information brings greater performance gains when the textual information is limited.However, most previous works ignore to take advantage of the complete textual inputs and the limited textual inputs at the same time, which limits the overall performance.To solve this issue, we propose a mixup method termed Soul-Mix to enhance MMT by using visual information more effectively.We mix the predicted translations of complete textual input and the limited textual inputs.Experimental results on the Multi30K dataset of three translation directions show that our Soul-Mix significantly outperforms existing approaches and achieves new state-of-the-art performance with fewer parameters than some previous models.Besides, the strength of Soul-Mix is more obvious on more challenging MSCOCO dataset which includes more out-of-domain instances with lots of ambiguous verbs.

Multi-modal sarcasm detection aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic, which has received increasing attention due to the rapid growth of multi-modal posts on modern social media. However, mainstream models process the input of each modality in a holistic manner, resulting in redundant and unrefined information. Moreover, the representations of different modalities are entangled in one common latent space to perform complex cross-modal interactions, neglecting the heterogeneity and distribution gap of different modalities. To address these issues, we propose a novel framework DMMD (short for Disentangled Multi-grained Multi-modal Distilling) for multi-modal sarcasm detection, which conducts multi-grained knowledge distilling (i.e., intra-subspace and inter-subspace) based on the disentangled multi-modal representations. Concretely, the representations of each modality are disentangled explicitly into modality-agnostic/specific subspaces. Then we transfer cross-modal knowledge by conducting intra-subspace knowledge distilling in a self-adaptive pattern. We also apply mutual learning to regularize the underlying inter-subspace consistency. Extensive experiments on a commonly used benchmark demonstrate the efficacy of our DMMD over cutting-edge methods. More encouragingly, visualization results indicate the multi-modal representations display meaningful distributional patterns, and we hope it will be helpful for the community of multi-modal knowledge transfer.

Continuous learning requires adapting quickly to incoming tasks while avoiding catastrophic forgetting. Typical solutions resort to a rehearsal buffer to replay old data, which is intractable to apply in real-world scenarios with limited memory and inaccessible privacy. Recently, with the emergence of large-scale pre-trained models, prompting methods have rapidly become a popular rehearsal-free alternative to rehearsal-based methods. The core of prmopting is to encode knowledge leveraging a set of parameters, however, knowledge decoupling and complementarity still remain some challenges. To tackle these challenges, this paper presents a KnowledgeComplementary Prompting approach, KC-Prompt, which end-to-end integrates and releases the task-invariant and task-specific knowledge for the ViT backbone. KC-Prompt designs knowledge maintenance and knowledge sharing mechanisms to form complementary prompt generators. In addition, we employ a components weighting method to instantiate prompt generators, making the training process fully differentiable. Sufficient experiments on CIFAR-100 and Split ImageNet-R benchmarks demonstrate the superiority of KC-Prompt in the challenging and realistic class-incremental learning setting.

Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query. The primary challenge lies in bridging the substantial gap between vision and language modalities, especially when dealing with limited large-scale datasets. In this paper, we introduce a CLIP-based Synergistic Knowledge Transfer (CSKT) approach for TPR. Specifically, to explore the CLIP’s knowledge on input side, we first propose a Bidirectional Prompts Transferring (BPT) module constructed by text-to-image and image-to-text bidirectional prompts and coupling projections. Secondly, Dual Adapters Transferring (DAT) is designed to transfer knowledge on output side of Multi-Head Self-Attention (MHA) in vision and language. This synergistic two-way collaborative mechanism promotes the early-stage feature fusion and efficiently exploits the existing knowledge of CLIP. CSKT outperforms the state-of-the-art approaches across three benchmark datasets when the training parameters merely account for 7.4% of the entire model, demonstrating its remarkable efficiency, effectiveness and generalization.

Multi-intent spoken language understanding (SLU) has garnered increasing attention since it can handle complex utterances expressing multiple intents in real-world scenarios. However, existing joint models are disturbed by label statistical frequencies, or adopt homogeneous graphs to capture interactions between the different types (e.g., intent and slot) of label nodes, thereby limiting the performance. To overcome these limitations, we propose Dual Heterogeneous Graph Label Interaction for multi-intent SLU, named DHLG. Concretely, we propose a global static heterogeneous label graph interaction layer to model both intra- and inter-label statistical dependencies across the entire training corpus. Based on this, we introduce a local dynamic heterogeneous label graph layer to further facilitate adaptive interactions between intents and slots for each utterance. Extensive experiments and analyses on two widely-used benchmark datasets demonstrate the superiority of our proposed DHLG over state-of-the-art methods.

Multimodal machine translation (MMT) aims to exploit visual information to improve neural machine translation (NMT). It has been demonstrated that image captioning and object detection can further improve MMT. In this paper, to leverage image captioning and object detection more effectively, we propose a Dual-level ASymmetric Contrastive Learning (DAS-CL) framework. Specifically, we leverage image captioning and object detection to generate more pairs of visual inputs and textual inputs. At the utterance level, we introduce an image captioning model to generate more coarse-grained pairs. At the word level, we introduce an object detection model to generate more fine-grained pairs. To mitigate the negative impact of noise in generated pairs, we apply asymmetric contrastive learning at these two levels. Experiments on the Multi30K dataset of three translation directions demonstrate that DAS-CL significantly outperforms existing MMT frameworks and achieves new state-of-the-art performance. More encouragingly, further analysis displays that DAS-CL is more robust to irrelevant visual information.

With the rise of short videos, the demand for selecting appropriate background music (BGM) for a video has increased significantly, video-music retrieval (VMR) task gradually draws much attention by research community. As other cross-modal learning tasks, existing VMR approaches usually attempt to measure the similarity between the video and music in the feature space. However, they (1) neglect the inevitable label noise; (2) neglect to enhance the ability to capture critical video clips. In this paper, we propose a novel saliency-based self-training framework, which is termed SSVMR. Specifically, we first explore to fully make use of the information containing in the training dataset by applying a semi-supervised method to suppress the adverse impact of label noise problem, where a self-training approach is adopted. In addition, we propose to capture the saliency of the video by mixing two videos at span level and preserving the locality of the two original videos. Inspired by back translation in NLP, we also conduct back retrieval to obtain more training data. Experimental results on MVD dataset show that our SSVMR achieves the state-of-the-art performance by a large margin, obtaining a relative improvement of 34.8% over the previous best model in terms of R@1.

The recent video grounding works attempt to introduce vanilla contrastive learning into video grounding. However, we claim that this naive solution is suboptimal. Contrastive learning requires two key properties: (1) alignment of features of similar samples, and (2) uniformity of the induced distribution of the normalized features on the hypersphere. Due to two annoying issues in video grounding: (1) the coexistence of some visual entities in both ground truth and other moments, i.e. semantic overlapping; (2) only a few moments in the video are annotated, i.e. sparse annotation dilemma, vanilla contrastive learning is unable to model the correlations between temporally distant moments and learned inconsistent video representations. Both characteristics lead to vanilla contrastive learning being unsuitable for video grounding. In this paper, we introduce Geodesic and Game Localization (G2L), a semantically aligned and uniform video grounding framework via geodesic and game theory. We quantify the correlations among moments leveraging the geodesic distance that guides the model to learn the correct cross-modal representations. Furthermore, from the novel perspective of game theory, we propose semantic Shapley interaction based on geodesic distance sampling to learn fine-grained semantic alignment in similar moments. Experiments on three benchmarks demonstrate the effectiveness of our method.

Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establishing global correspondences between the image (e.g., Chest X-ray) and its related report and local alignments between image patches and keywords remains challenging. To this end, we propose an Unify, Align and then Refine (UAR) approach to learn multi-level cross-modal alignments and introduce three novel modules: Latent Space Unifier (LSU), Cross-modal Representation Aligner (CRA) and Text-to-Image Refiner (TIR). Specifically, LSU unifies multimodal data into discrete tokens, making it flexible to learn common knowledge among modalities with a shared network. The modality-agnostic CRA learns discriminative features via a set of orthonormal basis and a dual-gate mechanism first and then globally aligns visual and textual representations under a triplet contrastive loss. TIR boosts token-level local alignment via calibrating text-to-image attention with a learnable mask. Additionally, we design a two-stage training procedure to make UAR gradually grasp cross-modal alignments at different levels, which imitates radiologists’ workflow: writing sentence by sentence first and then checking word by word. Extensive experiments and analyses on IU-Xray and MIMIC-CXR benchmark datasets demonstrate the superiority of our UAR against varied state-of-the-art methods.