Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data

TL;DR

This paper introduces a new evaluation task and dataset for video-text understanding, revealing current models' limitations and proposing a large language model-based approach to improve their comprehension of actions in videos.

Contribution

The paper proposes RCAD, a novel evaluation task with the Feint6K dataset, and introduces LLM-teacher, a method leveraging large language models to enhance video-text model understanding.

Findings

Current models are easily fooled by counterfactual data.

Models lag behind human performance on RCAD.

LLM-teacher improves action semantics learning.

Abstract

Recent video-text foundation models have demonstrated strong performance on a wide variety of downstream video understanding tasks. Can these video-text models genuinely understand the contents of natural videos? Standard video-text evaluations could be misleading as many questions can be inferred merely from the objects and contexts in a single frame or biases inherent in the datasets. In this paper, we aim to better assess the capabilities of current video-text models and understand their limitations. We propose a novel evaluation task for video-text understanding, namely retrieval from counterfactually augmented data (RCAD), and a new Feint6K dataset. To succeed on our new evaluation task, models must derive a comprehensive understanding of the video from cross-frame reasoning. Analyses show that previous video-text foundation models can be easily fooled by counterfactually augmented data and are far behind human-level performance. In order to narrow the gap between video-text models and human performance on RCAD, we identify a key limitation of current contrastive approaches on video-text data and introduce LLM-teacher, a more effective approach to learn action semantics by leveraging knowledge obtained from a pretrained large language model. Experiments and analyses show that our approach successfully learn more discriminative action embeddings and improves results on Feint6K when applied to multiple video-text models. Our Feint6K dataset and project page is available at https://feint6k.github.io.