Watch and Listen: Understanding Audio-Visual-Speech Moments with Multimodal LLM (2505.18110v2)

Abstract: Humans naturally understand moments in a video by integrating visual and auditory cues. For example, localizing a scene in the video like "A scientist passionately speaks on wildlife conservation as dramatic orchestral music plays, with the audience nodding and applauding" requires simultaneous processing of visual, audio, and speech signals. However, existing models often struggle to effectively fuse and interpret audio information, limiting their capacity for comprehensive video temporal understanding. To address this, we present TriSense, a triple-modality LLM designed for holistic video temporal understanding through the integration of visual, audio, and speech modalities. Central to TriSense is a Query-Based Connector that adaptively reweights modality contributions based on the input query, enabling robust performance under modality dropout and allowing flexible combinations of available inputs. To support TriSense's multimodal capabilities, we introduce TriSense-2M, a high-quality dataset of over 2 million curated samples generated via an automated pipeline powered by fine-tuned LLMs. TriSense-2M includes long-form videos and diverse modality combinations, facilitating broad generalization. Extensive experiments across multiple benchmarks demonstrate the effectiveness of TriSense and its potential to advance multimodal video analysis. Code and dataset will be publicly released.

Understanding Audio-Visual-Speech Integration in Multimodal LLMs

In the pursuit of optimizing LLMs for robust multimodal video analysis, the paper titled "Watch and Listen: Understanding Audio-Visual-Speech Moments with Multimodal LLM" introduces TriSense, a multimodal LLM designed to integrate visual, audio, and speech inputs. This essay outlines the objectives, methodology, and findings presented in this work, focusing on the model's design and the innovative dataset developed to support its implementation.

Objectives and Challenges

Modern video understanding systems require the integration of multiple data streams to interpret events comprehensively. However, existing multimodal LLMs often fall short, particularly in scenarios where audio or speech inputs are missing or noisy. This paper addresses two core challenges: the scarcity of high-quality, fully annotated multimodal datasets and the lack of modality adaptability in current models. These challenges limit the ability of models to generalize across diverse real-world scenarios effectively.

TriSense Architecture

The TriSense model introduces a sophisticated architecture that leverages a Query-Based Connector, which dynamically adjusts the importance of each modality based on the input query. This enables the model to deliver consistent performance under varying conditions, including modality dropout. TriSense further enhances temporal understanding by incorporating a specialized Time Encoder to capture fine-grained temporal dependencies essential for complex video tasks.

Dataset Development: TriSense-2M

TriSense-2M is a large-scale multimodal dataset comprising over 2 million samples, characterized by long-duration videos and diverse modality combinations. The dataset was generated using an automated pipeline involving fine-tuned LLMs trained to synthesize high-quality omni-modal annotations from modality-specific captions. The dataset's design facilitates robust training across different modality configurations, ultimately supporting the generalizability and resilience of multimodal models in practical applications.

Experimentation and Results

Comprehensive experiments demonstrate TriSense's capabilities across two primary tasks: video segment captioning and moment retrieval. Utilizing metrics such as BLEU-4 and CIDEr for captioning tasks, and Recall@IoU for retrieval tasks, TriSense exhibits superiority over existing models, including those specifically designed for video temporal grounding. Remarkably, despite processing fewer frames, TriSense achieves competitive performance in visual-only settings, highlighting its adaptability.

Implications for Future Research

The advancements highlighted in this work emphasize the potential of multimodal LLMs to transform video temporal understanding. By addressing real-world scenarios where modalities may vary, TriSense sets a precedent for future models aiming to achieve nuanced video analysis. The TriSense-2M dataset presents an invaluable resource for developing robust multimodal systems capable of operating in diverse environments.

Conclusion

The paper "Watch and Listen: Understanding Audio-Visual-Speech Moments with Multimodal LLM" outlines decisive steps toward overcoming current limitations in multimodal video analysis. TriSense's innovative approach to modality integration and its supporting dataset demonstrate an evolution in how LLMs can comprehensively interpret complex video data. This work not only advances theoretical understanding but also unlocks practical applications for AI systems tasked with real-world video interpretations. The implications of these findings will likely inspire future research directed towards refining the interplay of multiple modalities in LLMs, promoting more profound insights and versatile applications in AI video analysis.