Look, Listen and Segment: Towards Weakly Supervised Audio-visual Semantic Segmentation
Abstract
Audio-Visual Semantic Segmentation (AVSS) aligns audio and video at the pixel level but requires costly per-frame annotations. We introduce Weakly Supervised Audio-Visual Semantic Segmentation (WSAVSS), which uses only video-level labels to generate per-frame semantic masks of sounding objects. We decompose WSAVSS into looking, listening, and segmentation, and propose Progressive Cross-modal Alignment for Semantics (PCAS) with two modules: *Looking-before-Listening* and *Listening-before-Segmentation*. PCAS builds a classification task to train the audio-visual encoder using video labels, injects visual semantic prompts to enhance frame-level audio understanding, and then applies progressive contrastive alignment to map audio categories to image regions without mask annotations. Experiments show PCAS achieves state-of-the-art performance among weakly supervised methods on AVS and remains competitive with fully supervised baselines on AVSS, validating its effectiveness.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a novel weakly supervised framework for audio-visual semantic segmentation that effectively aligns audio and visual features without requiring dense annotations. This work represents a significant step forward in the field of audio-visual understanding, providing a robust methodology and promising results that could influence future research and applications.
Methodology
The methodology presented in this paper is innovative, particularly in its decomposition of the WSAVSS task into three distinct phases: looking, listening, and segmentation. The introduction of Temporal Visual Prompting (TVP) to enhance audio understanding through visual cues is a novel approach that leverages the inherent relationships between audio and visual modalities. The Progressive Cross-modal Alignment for Semantics (PCAS) framework, which combines instance-wise and token-wise contrastive learning, is well-conceived and addresses the challenge of aligning audio and visual features without requiring dense annotations. This progressive alignment strategy is a significant advancement over existing methods, making it a valuable contribution to the field.
Results & evaluation
The experiments are comprehensive, demonstrating the effectiveness of the proposed method through comparisons with both weakly supervised and fully supervised baselines. The use of multiple datasets and the reporting of mean IoU and F-score metrics provide a robust evaluation of the model's performance. The ablation studies effectively highlight the contributions of each module within the proposed framework, reinforcing the claims of improved performance. However, the absence of a demo or project URL limits the accessibility of the results for further validation by the community.
Reproducibility
While the paper provides a detailed description of the methodology and experimental setup, it lacks specific implementation details such as code availability or dataset access instructions. The absence of these resources may hinder reproducibility. Clearer guidelines on how to replicate the experiments would enhance the paper's impact.
Limitations
One limitation of the study is the reliance on video-level labels, which, while reducing annotation costs, may not capture the full complexity of audio-visual interactions. Additionally, the paper does not address potential biases in the datasets used, which could affect the generalizability of the results. The performance on more complex scenes with overlapping sounds and visuals could also be explored further.
Broader impact
The proposed WSAVSS framework has significant implications for applications in multimedia content analysis, human-computer interaction, and assistive technologies. By reducing the need for extensive annotations, this research can facilitate advancements in real-time audio-visual processing systems, enhancing accessibility and user experience in various domains. The approach could also inspire further research into weakly supervised learning paradigms across different modalities. The main contribution of this paper is the introduction of a novel weakly supervised framework for audio-visual semantic segmentation that effectively aligns audio and visual features without requiring dense annotations. This work represents a significant step forward in the field of audio-visual understanding, providing a robust methodology and promising results that could influence future research and applications.
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