CVPR 2025 论文列表

Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However, most approaches rely on global audio representations that fail to capture fine-grained temporal correspondences with visual frames.Additionally, existing methods often struggle with conflicting optimization objectives when trying to jointly learn reconstruction and cross-modal alignment. In this work, we propose CAV-MAE Sync as a simple yet effective extension of the original CAV-MAE framework for self-supervised audio-visual learning. We address three key challenges: First, we tackle the granularity mismatch between modalities by treating audio as a temporal sequence aligned with video frames, rather than using global representations. Second, we resolve conflicting optimization goals by separating contrastive and reconstruction objectives through dedicated global tokens. Third, we improve spatial localization by introducing learnable register tokens that reduce semantic load on patch tokens. We evaluate the proposed approach on AudioSet, VGG Sound, and the ADE20K Sound dataset on zero-shot retrieval, classification and localization tasks demonstrating state-of-the-art performance and outperforming more complex architectures. Code available at https://github.com/edsonroteia/cav-mae-sync.

Omnidirectional videos (ODVs) present distinct challenges for accurate audio-visual saliency prediction due to their immersive nature, which combines spatial audio with panoramic visuals to enhance the user experience. While auditory cues are crucial for guiding visual attention across the panoramic scene, the interaction between audio and visual stimuli in ODVs remains underexplored. Existing models primarily focus on spatiotemporal visual cues and treat audio signals separately from their spatial and temporal contexts, often leading to misalignments between audio and visual content and undermining temporal consistency across frames. To bridge these gaps, we propose a novel audio-induced saliency prediction model for ODVs that holistically integrates audio and visual inputs through a multi-modal encoder, an audio-visual interaction module, and an audio-visual transformer. Unlike conventional methods that isolate audio cue locations and attributes, our model employs a query-based framework, where learnable audio queries capture comprehensive audio-visual dependencies, thus enhancing saliency prediction by dynamically aligning with audio cues. Besides, we introduce a novel consistency loss to enforce temporal coherence in saliency regions across frames. Extensive experiments demonstrate that our model outperforms state-of-the-art methods in predicting audio-visual salient regions in ODVs, establishing its robustness and superior performance.

Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed in resource-constrained mobile devices, due to suffering from either few efficiency gains or significant accuracy drops. In this paper, we propose a new deCoupled duAl-interactive lineaR attEntion (CARE) mechanism, revealing that features' decoupling and interaction can fully unleash the power of linear attention. We first propose an asymmetrical feature decoupling strategy that asymmetrically decouples the learning process for local inductive bias and long-range dependencies, thereby preserving sufficient local and global information while effectively enhancing the efficiency of models. Then, a dynamic memory unit is employed to maintain critical information along the network pipeline. Moreover, we design a dual interaction module to effectively facilitate interaction between local inductive bias and long-range information as well as among features at different layers. By adopting a decoupled learning way and fully exploiting complementarity across features, our method can achieve both high efficiency and accuracy. Extensive experiments on ImageNet-1K, COCO, and ADE20K datasets demonstrate the effectiveness of our approach, e.g., achieving 78.4/82.1% top-1 accuracy on ImagegNet-1K at the cost of only 0.7/1.9 GMACs. Codes will be released on github.

Recent advancements in visual object tracking have shifted towards a sequential generation paradigm, where object deformation and motion exhibit strong temporal dependencies. Despite the importance of these dependencies, widely adopted image-level pretrained backbones barely capture the dynamics in the consecutive video, which is the essence of tracking. Thus, we propose AutoRegressive Sequential Pretraining (ARP), an unsupervised spatio-temporal learner, via generating the evolution of object appearance and motion in video sequences.Our method leverages a diffusion model to autoregressively generate the future frame appearance, conditioned on historical embeddings extracted by a general encoder. Furthermore, to ensure trajectory coherence, the same encoder is employed to learn trajectory consistency by generating coordinate sequences in a reverse autoregressive fashion, a process we term back-tracking. Further, we integrate the pretrained ARP into ARTrackV2, creating ARPTrack, which is further fine-tuned for tracking tasks. ARPTrack achieves state-of-the-art performance across multiple benchmarks, becoming the first tracker to surpass 80% AO on GOT-10k, while maintaining high efficiency. These results demonstrate the effectiveness of our approach in capturing temporal dependencies for continuous video tracking.

Audio-visual event localization (AVEL) aims to identify both the category and temporal boundaries of events that are both audible and visible in unconstrained videos. However, the inherent semantic gap between heterogeneous modalities often leads to semantic inconsistency. In this paper, we propose a novel Audio-Visual Semantic Graph Network (AVSGN) to facilitate cross-modal alignment and cross-temporal interaction. Unlike previous approaches (e.g., audio-guided, visual-guided, or both), we introduce shared semantic textual labels to bridge the semantic gap between audio and visual modalities. Specifically, we present a cross-modal semantic alignment (CMSA) module to explore the complementary relationships across heterogeneous modalities (i.e., visual, audio, and text), promoting the convergence of multimodal distributions into a unified semantic space. Additionally, in order to capture cross-temporal dependencies sufficiently, we devise a cross-modal graph interaction (CMGI) module which disentangles complicated interactions across modalities into three complementary subgraphs. Extensive experiments on the AVE dataset comprehensively demonstrate the superiority and effectiveness of the proposed model in both fully- and weakly-supervised AVE settings.

In this paper, we propose a new multi-modal task, termed audio-visual instance segmentation (AVIS), which aims to simultaneously identify, segment and track individual sounding object instances in audible videos. To facilitate this research, we introduce a high-quality benchmark named AVISeg, containing over 90K instance masks from 26 semantic categories in 926 long videos. Additionally, we propose a strong baseline model for this task. Our model first localizes sound source within each frame, and condenses object-specific contexts into concise tokens. Then it builds long-range audio-visual dependencies between these tokens using window-based attention, and tracks sounding objects among the entire video sequences. Extensive experiments reveal that our method performs best on AVISeg, surpassing the existing methods from related tasks. We further conduct the evaluation on several multi-modal large models. Unfortunately, they exhibits subpar performance on instance-level sound source localization and temporal perception. We expect that AVIS will inspire the community towards a more comprehensive multi-modal understanding. Dataset and code is available at https://github.com/ruohaoguo/avis.

Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degree of ambiguity. However, existing approaches are deterministic and overlook this aspect. In this paper, we aim to close the gap between traditional geolocalization and modern generative methods. We propose the first generative geolocation approach based on diffusion and Riemannian flow matching, where the denoising process operates directly on the Earth's surface. Our model achieves state-of-the-art performance on three visual geolocation benchmarks: OpenStreetView-5M, YFCC-100M, and iNat21. In addition, we introduce the task of probabilistic visual geolocation, where the model predicts a probability distribution over all possible locations instead of a single point. We introduce new metrics and baselines for this task, demonstrating the advantages of our diffusion-based approach. Codes and models will be made available.

Semantic future prediction is important for autonomous systems navigating dynamic environments. This paper introduces FUTURIST, a method for multimodal future semantic prediction that uses a unified and efficient visual sequence transformer architecture. Our approach incorporates a multimodal masked visual modeling objective and a novel masking mechanism designed for multimodal training. This allows the model to effectively integrate visible information from various modalities, improving prediction accuracy. Additionally, we propose a VAE-free hierarchical tokenization process, which reduces computational complexity, streamlines the training pipeline, and enables end-to-end training with high-resolution, multimodal inputs. We validate FUTURIST on the Cityscapes dataset, demonstrating state-of-the-art performance in future semantic segmentation for both short- and mid-term forecasting. We provide the implementation code and model weights at https://github.com/Sta8is/FUTURIST.

In the domain of audio-visual event perception, which focuses on the temporal localization and classification of events across distinct modalities (audio and visual), existing approaches are constrained by the vocabulary available in their training data. This limitation significantly impedes their capacity to generalize to novel, unseen event categories. Furthermore, the annotation process for this task is labor-intensive, requiring extensive manual labeling across modalities and temporal segments, limiting the scalability of current methods. Current state-of-the-art models ignore the shifts in event distributions over time, reducing their ability to adjust to changing video dynamics. Additionally, previous methods rely on late fusion to combine audio and visual information. While straightforward, this approach results in a significant loss of multimodal interactions. To address these challenges, we propose Audio-Visual Adaptive Video Analysis (AV2A), a model-agnostic approach that requires no further training and integrates a score-level fusion technique to retain richer multimodal interactions. AV2A also includes a within-video label shift algorithm, leveraging input video data and predictions from prior frames to dynamically adjust event distributions for subsequent frames. Moreover, we present the first training-free, open-vocabulary baseline for audio-visual event perception, demonstrating that AV2A achieves substantial improvements over naive training-free baselines. We demonstrate the effectiveness of AV2A on both zero-shot and weakly-supervised state-of-the-art methods, achieving notable improvements in performance metrics over existing approaches.

In this paper, a novel benchmark for audio-visual question answering continual learning (AVQACL) is introduced, aiming to study fine-grained scene understanding and spatial-temporal reasoning in videos under a continual learning setting. To facilitate this multimodal continual leaning task, we create two audio-visual question answering continual learning datasets, named Split-AVQA and Split-MUSIC-AVQA based on the AVQA and MUSIC-AVQA datasets, respectively. The experimental results suggest that the model exhibits limited cognitive and reasoning abilities and experiences catastrophic forgetting when processing three modalities simultaneously in a continuous data stream. To address above challenges, we propose a novel continual learning method that incorporates question-guided cross-modal information fusion (QCIF) to focus on question-relevant details for improved feature representation and task-specific knowledge distillation with spatial-temporal feature constraints (TKD-STFC) to preserve the spatial-temporal reasoning knowledge acquired from previous dynamic scenarios. Furthermore, a question semantic consistency constraint (QSCC) is employed to ensure that the model maintains a consistent understanding of question semantics across tasks throughout the continual learning process. Extensive experimental results on Split-AVQA and Split-MUSIC-AVQA datasets illustrate that our method achieves state-of-the-art audio-visual question answering continual learning performance. The code is available at https://github.com/kx-wu/CVPR2025_AVQACL.

Memory-based trackers are video object segmentation methods that form the target model by concatenating recently tracked frames into a memory buffer and localize the target by attending the current image to the buffered frames. While already achieving top performance on many benchmarks, it was the recent release of SAM2 that placed memory-based trackers into focus of the visual object tracking community. Nevertheless, modern trackers still struggle in the presence of distractors. We argue that a more sophisticated memory model is required, and propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness. The resulting tracker is denoted as DAM4SAM. We also propose a new distractor-distilled DiDi dataset to study the distractor problem better. DAM4SAM outperforms SAM2.1 and related SAM memory extensions on seven benchmarks and sets a solid new state-of-the-art on six of them.

Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more options for visual classification tasks. Despite their success, existing visual delta-tuning art fails to exceed the upper limit of full fine-tuning on challenging tasks. To find a competitive alternative to full fine-tuning, we propose the Multi-cognitive Visual Adapter (Mona) tuning, a novel adapter-based tuning method. First, we introduce multiple vision-friendly filters into the adapter to enhance its ability for processing visual signals, while previous methods mainly rely on language-friendly linear filters. Second, we add the scaled layernorm in the adapter to regulate the distribution of input features for visual filters. To fully demonstrate the practicality and generality of Mona, we conduct experiments on representative visual tasks, including instance segmentation on COCO, semantic segmentation on ADE20K, object detection on Pascal VOC, oriented object detection on DOTA/STAR, and image classification on three common datasets. Exciting results illustrate that Mona surpasses full fine-tuning on all these tasks by tuning less than 5% params of the backbone, and is the only delta-tuning method outperforming full fine-tuning on all tasks. For example, Mona achieves 1% performance gain on the COCO compared to full fine-tuning. Comprehensive results suggest that Mona-tuning is more suitable for retaining and utilizing the capabilities of pre-trained models than full fine-tuning. The code is publicly available on https://github.com/Leiyi-Hu/mona.