arXiv:2406.15704v1 Announce Type: new Abstract: Speech understanding as an element of the more generic video understanding using audio-visual large language models (av-LLMs) is a crucial yet understudied aspect. This paper proposes video-SALMONN, a single end-to-end av-LLM for video processing, which can understand not only visual frame sequences, audio events and music, but speech as well. To obtain fine-grained temporal information required by speech understanding, while keeping efficient for other video elements, this paper proposes a novel multi-resolution causal Q-Former (MRC Q-Former) structure to connect pre-trained audio-visual encoders and the backbone large language model. Moreover, dedicated training approaches including the diversity loss and the unpaired audio-visual mixed training scheme are proposed to avoid frames or modality dominance. On the introduced speech-audio-visual evaluation benchmark, video-SALMONN achieves more than 25% absolute accuracy improvements on the video-QA task and over 30% absolute accuracy improvements on audio-visual QA tasks with human speech. In addition, video-SALMONN demonstrates remarkable video comprehension and reasoning abilities on tasks that are unprecedented by other av-LLMs. Our training code and model checkpoints are available at texttt{url{https://github.com/bytedance/SALMONN/}}.
The article “Speech Understanding in Video Using Audio-Visual Large Language Models” introduces video-SALMONN, a groundbreaking end-to-end av-LLM (audio-visual large language model) for video processing. While speech understanding in videos is a vital aspect, it has received limited attention in research. This paper addresses this gap by proposing video-SALMONN, which can comprehend not only visual frame sequences, audio events, and music but also speech. To capture the precise temporal information required for speech understanding while maintaining efficiency for other video elements, the paper introduces a novel multi-resolution causal Q-Former (MRC Q-Former) structure that connects pre-trained audio-visual encoders and the backbone large language model. The paper also presents dedicated training approaches, including diversity loss and unpaired audio-visual mixed training, to prevent frames or modality dominance. The evaluation of video-SALMONN on a speech-audio-visual benchmark showcases its significant improvements in accuracy, surpassing other av-LLMs by more than 25% in video-QA tasks and over 30% in audio-visual QA tasks involving human speech. Additionally, video-SALMONN demonstrates exceptional video comprehension and reasoning abilities in tasks that were previously unexplored by other av-LLMs. The paper concludes by providing access to the training code and model checkpoints for video-SALMONN.
Exploring the Potential of Video-SALMONN: Advancing Video Understanding with AV-LLMs
In recent years, the field of video understanding has seen significant advancements. With the advent of large language models (LLMs) and the integration of audio-visual information, the ability to comprehend video content has been greatly enhanced. However, one aspect that has received less attention is the understanding of speech within videos. Speech understanding is a crucial element in video comprehension, and addressing this gap in research can open up new possibilities for improved video analysis and interpretation.
In a recent paper titled “Video-SALMONN: Enhancing Video Processing with AV-LLMs,” a team of researchers proposes a novel approach to video understanding. Their solution, video-SALMONN, is a single end-to-end AV-LLM that combines visual frame sequences, audio events, music, and speech in its interpretation of video content. By incorporating fine-grained temporal information necessary for effective speech understanding, video-SALMONN offers a comprehensive approach to video processing that surpasses current models.
Introducing the Multi-Resolution Causal Q-Former (MRC Q-Former)
To enable video-SALMONN’s speech understanding capabilities without sacrificing efficiency in processing other video elements, the researchers introduce a new component called the Multi-Resolution Causal Q-Former (MRC Q-Former). This structure acts as a bridge between pre-trained audio-visual encoders and the backbone large language model, allowing for seamless integration of speech understanding into the overall video comprehension process.
The MRC Q-Former adopts a multi-resolution approach to capture both short-term and long-term temporal dependencies within the video. By utilizing causal connections, the model can effectively predict future speech events based on past and present visual and audio cues. This hierarchical structure enhances the model’s ability to extract relevant context and temporal information specific to speech, enabling more accurate speech understanding within video content.
Addressing Biases with Dedicated Training Approaches
One of the key challenges in training AV-LLMs for speech understanding is the potential dominance of frames or modalities. To tackle this issue, the researchers propose dedicated training approaches, including the diversity loss and the unpaired audio-visual mixed training scheme.
The diversity loss mechanism encourages the model to capture a wide range of speech variations by penalizing redundancy in the generated responses. This approach promotes a more diverse and contextually rich understanding of speech, reducing the risk of biased or limited interpretations.
The unpaired audio-visual mixed training scheme addresses the challenge of aligning audio and visual inputs during training. By randomly pairing audio and visual streams from different videos, the model is exposed to a more diverse range of audio-visual combinations. This training strategy aids in reducing modality dominance and encourages the model to focus on the content itself rather than relying on pairing cues.
Unprecedented Achievements and Broad Application Potential
To evaluate the performance of video-SALMONN, the researchers designed a speech-audio-visual evaluation benchmark. The results showed that video-SALMONN achieved more than 25% absolute accuracy improvements on the video-QA task and over 30% absolute accuracy improvements on audio-visual QA tasks involving human speech. These remarkable improvements highlight the effectiveness of the proposed approach in enhancing video comprehension.
Beyond speech understanding, video-SALMONN also demonstrated exceptional comprehension and reasoning abilities on tasks that were previously unmatched by other AV-LLMs. The potential applications of video-SALMONN extend to various fields such as video summarization, content recommendation systems, and automated video transcription, where accurate and nuanced understanding of video content is paramount.
As the field of video understanding continues to evolve, solutions like video-SALMONN pave the way for more advanced and comprehensive approaches to interpreting video content. By addressing the long-standing gap in speech understanding within videos, video-SALMONN opens up new avenues for research and innovation.
For those interested in exploring video-SALMONN further, the researchers have made the training code and model checkpoints available on their GitHub repository, accessible here.
The paper titled “Speech understanding as an element of the more generic video understanding using audio-visual large language models (av-LLMs)” introduces an innovative approach called video-SALMONN. This approach aims to enhance the understanding of speech within video content by leveraging audio-visual large language models.
The authors highlight the importance of speech understanding in video processing and emphasize that this aspect has been relatively understudied. To address this gap, the proposed video-SALMONN model is designed as a single end-to-end av-LLM capable of comprehending visual frame sequences, audio events, music, and speech.
One key contribution of this paper is the introduction of a multi-resolution causal Q-Former (MRC Q-Former) structure. This structure connects pre-trained audio-visual encoders with the backbone large language model, enabling the extraction of fine-grained temporal information necessary for speech understanding. Importantly, this structure ensures efficiency for processing other video elements while focusing on speech.
To improve the training process and avoid dominance of certain frames or modalities, the authors propose dedicated training approaches. These include the diversity loss and the unpaired audio-visual mixed training scheme. These techniques aim to enhance the model’s ability to handle various types of video content and ensure balanced learning across different modalities.
The evaluation of video-SALMONN on a speech-audio-visual benchmark demonstrates its effectiveness. Notably, the model achieves significant accuracy improvements of more than 25% on the video-QA task and over 30% on audio-visual QA tasks involving human speech. These results highlight the potential of video-SALMONN in enhancing speech understanding within video content.
Furthermore, the paper highlights the remarkable video comprehension and reasoning abilities of video-SALMONN. It outperforms other av-LLMs on tasks that were previously challenging or unexplored. This suggests that video-SALMONN has the potential to advance the state-of-the-art in video understanding and reasoning.
Overall, this paper presents a comprehensive approach, video-SALMONN, that addresses the understudied aspect of speech understanding within video content. The proposed model, with its multi-resolution causal Q-Former structure and dedicated training approaches, shows promising results in improving accuracy and achieving remarkable video comprehension and reasoning abilities. The availability of the training code and model checkpoints on GitHub further enhances the reproducibility and accessibility of this work.
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