Uncertainty-Guided Self-Questioning and Answering for Video-Language Alignment

TL;DR

This paper introduces BoViLA, a self-training framework using LLM-based self-questioning and answering to improve video-language alignment in VideoQA tasks, with uncertainty filtering to ensure quality.

Contribution

It is the first to explore LLM-based self-training for modality alignment, enhancing VideoQA performance through uncertainty-aware question filtering.

Findings

Outperforms state-of-the-art on five VideoQA benchmarks.

Effective use of EDL for filtering low-quality self-generated questions.

Demonstrates the generality of the self-training framework.

Abstract

The development of multi-modal models has been rapidly advancing, with some demonstrating remarkable capabilities. However, annotating video-text pairs remains expensive and insufficient. Take video question answering (VideoQA) tasks as an example, human annotated questions and answers often cover only part of the video, since the corresponding text is often short and monotonous, leading to underutilization of video. To address this, we propose a Bootstrapping Video-Language Alignment framework (BoViLA), a self-training method that augments question samples during training process through LLM-based self-questioning and answering, which help model exploit video information and the internal knowledge of LLMs more thoroughly to improve modality alignment. However, low-quality self-generated questions may instead contaminate the performance, especially in the early stages of training, as we have observed in our experiments. To filter bad self-generated questions, we introduce Evidential Deep Learning (EDL) to estimate uncertainty and assess the quality of self-generated questions by evaluating the modality alignment within the context. To the best of our knowledge, this work is the first to explore LLM-based self-training frameworks for modality alignment. We evaluate BoViLA on five strong VideoQA benchmarks, where it outperforms several state-of-the-art methods and demonstrate its effectiveness and generality. Additionally, we provide extensive analyses of the self-training framework and the EDL-based uncertainty filtering mechanism. The code will be made available.