EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing
Abstract
Weakly supervised Audio-Visual Video Parsing (AVVP) aims to recognize and temporally localize audio, visual, and audio-visual events in videos using only coarse-grained labels. Faced with the challenging task settings, existing research advances along two main paths: pre-training pseudo-label generators for fine-grained cross-modal semantic guidance, or refining AVVP model architectures to enhance audio-visual fusion. However, since audio and visual signals are typically unaligned, achieving accurate video parsing fundamentally relies on precise perception of uni-modal events. Yet these multi-modal focused strategies excessively emphasize multi-modal fusion while inadequately guiding and preserving uni-modal semantics, resulting in noisy pseudo-labels and sub-optimal video parsing performance. This paper proposes a novel framework that enhances uni-modal representations for both the pseudo-label generator and the AVVP model. Specifically, we introduce a similarity-based label migration approach to annotate pre-training data, thereby enabling the pseudo-label generator to better understand uni-modal events. We also employ a soft-constrained manner to refine modeling of uni-modal features in parallel with multi-modal fusion. These designs enable coordinated attention to both uni-modal and cross-modal representations, thus boosting the localization performance for events. Extensive experiments show that our method outperforms state-of-the-art methods in both pseudo-label and AVVP performance.
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 the EAR framework, which effectively enhances uni-modal representations for weakly supervised audio-visual video parsing, achieving state-of-the-art performance in the field. The comprehensive analysis of the technical contributions, methodology, and significance highlights its potential to advance research in multi-modal learning and video understanding.
Methodology
The proposed EAR framework introduces a novel approach to enhance uni-modal representations in weakly supervised audio-visual video parsing. The methodology is well-structured, incorporating a similarity-based label migration technique for pre-training the pseudo-label generator and a soft-constrained modeling approach for the AVVP model. This dual focus on both uni-modal and multi-modal event detection is innovative, addressing a significant gap in existing literature where multi-modal fusion often overshadows the importance of individual modality understanding. The use of asymmetric temporal modeling and the introduction of dependencies among events further strengthen the framework's design. However, the complexity of the model may pose challenges in practical applications.
Results & evaluation
The experiments are extensive, utilizing a large-scale dataset for pre-training and a well-defined benchmark for evaluation. The results demonstrate significant performance improvements over state-of-the-art methods in both pseudo-label generation and AVVP tasks, indicating the effectiveness of the proposed methods. The paper provides detailed comparisons with existing techniques, showcasing the advantages of the EAR framework. However, the reliance on specific datasets may limit the generalizability of the findings.
Reproducibility
The implementation details are adequately described, including the architecture, training procedures, and evaluation metrics. However, the absence of publicly available code or datasets limits reproducibility. Providing access to the trained models or a GitHub repository would enhance the ability of other researchers to validate and build upon this work.
Limitations
The paper acknowledges that the similarity-based label migration may introduce noise, particularly in visual modalities. This noise can affect the quality of pseudo-labels, potentially leading to suboptimal performance in certain scenarios. Additionally, the complexity of the model may hinder real-time applications, and the reliance on large datasets for training could limit its applicability in resource-constrained environments.
Broader impact
The proposed framework has significant implications for audio-visual understanding in various applications, including automated video editing, surveillance, and human-computer interaction. By improving the accuracy of event localization in videos, this research could enhance the capabilities of systems that rely on audio-visual data, leading to more intelligent and responsive technologies. The main contribution of this paper is the introduction of the EAR framework, which effectively enhances uni-modal representations for weakly supervised audio-visual video parsing, achieving state-of-the-art performance in the field. The comprehensive analysis of the technical contributions, methodology, and significance highlights its potential to advance research in multi-modal learning and video understanding.
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