IPFormer-VideoLLM: Enhancing Multi-modal Video Understanding for Multi-shot Scenes
This work addresses a specific challenge in multi-modal AI for video analysis, offering incremental improvements in handling multi-shot scenes.
The authors tackled the problem of VideoLLMs struggling with multi-shot video scenarios by introducing a new dataset, MultiClip-Bench, and a model, IPFormer-VideoLLM, which uses instance prompts to aggregate identity information, resulting in significant enhancements in multi-scene understanding and advantages across video benchmarks.
Video Large Language Models (VideoLLMs) have demonstrated remarkable understanding capabilities, but are found struggling to tackle multi-shot scenarios,e.g., video clips with varying camera angles or scene changes. This challenge can render failures such as instance identity forgetting and key frame negligence. In this work, we first attribute the challenge to the lack of multi-shot annotations among existing datasets and therefore we introduce a new dataset termed MultiClip-Bench, featuring dense descriptions and instruction-based question-answering pairs tailored for multi-shot scenarios. We empirically find that the training set significantly boosts the multi-shot performance, while the testing benchmark provides a reliable measure of the model capability in multi-shot scenarios. By further analyzing and discovering that current models only encode instance features in a discrete or lossy manner, at the risk of missing identity information, we then contribute a new model IPFormer-VideoLLM. Its key idea is the injection of instance-level features as instance prompts through an efficient attention-based connector. This allows for the aggregation of instance-specific information across scenes. Experiments demonstrate that our proposed dataset and model not only enhance the multi-scene video understanding significantly, but also offer distinct advantages across various video benchmarks.