Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning
Abstract
We introduce Perception Encoder Audiovisual, PE-AV, a new family of encoders for audio and video understanding trained with scaled contrastive learning. Built on PE, PE-AV makes several key contributions to extend representations to audio, and natively support joint embeddings across audio-video, audio-text, and video-text modalities. PE-AV's unified cross-modal embeddings enable novel tasks such as speech retrieval, and set a new state of the art across standard audio and video benchmarks. We unlock this by building a strong audiovisual data engine that synthesizes high-quality captions for O(100M) audio-video pairs, enabling large-scale supervision consistent across modalities. Our audio data includes speech, music, and general sound effects-avoiding single-domain limitations common in prior work. We exploit ten pairwise contrastive objectives, showing that scaling cross-modality and caption-type pairs strengthens alignment and improves zero-shot performance. We further develop PE-A-Frame by fine-tuning PE-AV with frame-level contrastive objectives, enabling fine-grained audio-frame-to-text alignment for tasks such as sound event detection.
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 PE-AV framework, which significantly advances the state of audiovisual perception through innovative contrastive learning techniques and large-scale data synthesis. This work is poised to impact various applications in multimodal machine learning and sets a new benchmark for future research in the field.
Methodology
The proposed methodology introduces the Perception Encoder Audiovisual (PE-AV) framework, which effectively leverages scaled contrastive learning to create unified cross-modal embeddings. The use of ten pairwise contrastive objectives is innovative, as it enhances the alignment across different modalities (audio, video, text) and allows for novel tasks such as speech retrieval. The integration of a large-scale audiovisual data engine that synthesizes high-quality captions for a vast number of audio-video pairs is a significant methodological advancement, addressing the limitations of prior works that often focused on single-domain data.
Results & evaluation
The experiments conducted demonstrate the effectiveness of PE-AV across various standard audio and video benchmarks, achieving state-of-the-art results. The evaluation of downstream applications, particularly in sound event detection, showcases the practical applicability of the model. However, the paper could benefit from more detailed descriptions of the datasets used and the specific metrics employed for evaluation, as well as comparisons with additional baselines to strengthen the claims of superiority.
Reproducibility
The paper lacks sufficient implementation details that would facilitate reproducibility. There is no mention of code availability or specific configurations used during training and evaluation, which are crucial for other researchers to replicate the results. Providing a project URL or a GitHub repository would significantly enhance the reproducibility of the findings.
Limitations
One limitation noted is the reliance on a large-scale dataset that may not be readily available to all researchers, potentially hindering further exploration of the proposed methods. Additionally, while the approach shows promise, the paper does not address potential biases in the data or the implications of using such a large-scale audiovisual dataset.
Broader impact
The implications of this research are significant, as it opens up new avenues for multimodal learning applications in areas such as human-computer interaction, content retrieval, and automatic captioning systems. The ability to align audio and video representations could lead to advancements in accessibility technologies and enhance the user experience in multimedia applications. The main contribution of this paper is the introduction of the PE-AV framework, which significantly advances the state of audiovisual perception through innovative contrastive learning techniques and large-scale data synthesis. This work is poised to impact various applications in multimodal machine learning and sets a new benchmark for future research in the field.
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