Audio-visual training for improved grounding in video-text LLMs

Recent advances in multimodal LLMs, have led to several video-text models being proposed for critical video-related tasks. However, most of the previous works support visual input only, essentially muting the audio signal in the video. Few models that support both audio and visual input, are not explicitly trained on audio data. Hence, the effect of audio towards video understanding is largely unexplored. To this end, we propose a model architecture that handles audio-visual inputs explicitly. We train our model with both audio and visual data from a video instruction-tuning dataset. Comparison with vision-only baselines, and other audiovisual models showcase that training on audio data indeed leads to improved grounding of responses. Figure 1: An example of improved grounding in the For better evaluation of audio-visual video-text LLM outputs, due to the additional audio models, we also release a human-annotated signal as input.