VideoDistill: Language-aware Vision Distillation for Video Question Answering

TL;DR

VideoDistill introduces a goal-driven, language-aware framework for VideoQA that enhances visual and answer generation by focusing on question-related visual cues, achieving state-of-the-art results and reducing language shortcut reliance.

Contribution

The paper presents a novel language-aware gating mechanism and a selective frame sampling strategy to improve visual question answering in videos, closely mimicking human reasoning.

Findings

Achieves state-of-the-art performance on multiple VideoQA benchmarks.

Effectively reduces reliance on language shortcuts in VideoQA.

Enhances focus on question-relevant visual information.

Abstract

Significant advancements in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although these image-language models can efficiently represent both video and language branches, they typically employ a goal-free vision perception process and do not interact vision with language well during the answer generation, thus omitting crucial visual cues. In this paper, we are inspired by the human recognition and learning pattern and propose VideoDistill, a framework with language-aware (i.e., goal-driven) behavior in both vision perception and answer generation process. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior, distinguishing it from previous research. Specifically, we develop a language-aware gating mechanism to replace the standard cross-attention, avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module, which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure the extraction of multi-grained visual semantics associated with the questions. We conduct experimental evaluations on various challenging video question-answering benchmarks, and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition, we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.