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Recent advancements in language-model-based video understanding have beenprogressing at a remarkable pace, spurred by the introduction of Large LanguageModels (LLMs). However, the focus of prior research has been predominantly ondevising a projection layer that maps video features to tokens, an approachthat is both rudimentary and inefficient. In our study, we introduce acutting-edge framework, VaQuitA, designed to refine the synergy between videoand textual information. At the data level, instead of sampling framesuniformly, we implement a sampling method guided by CLIP-score rankings, whichenables a more aligned selection of frames with the given question. At thefeature level, we integrate a trainable Video Perceiver alongside aVisual-Query Transformer (abbreviated as VQ-Former), which bolsters theinterplay between the input question and the video features. We also discoverthat incorporating a simple prompt, "Please be critical", into the LLM inputcan substantially enhance its video comprehension capabilities. Ourexperimental results indicate that VaQuitA consistently sets a new benchmarkfor zero-shot video question-answering tasks and is adept at producinghigh-quality, multi-turn video dialogues with users.

VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding