REVEAL: Relation-based Video Representation Learning for Video-Question-Answering

• URL: http://arxiv.org/abs/2504.05463v1
• Date: Mon, 07 Apr 2025 19:54:04 GMT
• Title: REVEAL: Relation-based Video Representation Learning for Video-Question-Answering
• Authors: Sofian Chaybouti, Walid Bousselham, Moritz Wolter, Hilde Kuehne,
• Abstract summary: We propose RElation-based rEpresentAtion Learning (REVEAL) to capture visual relation information.<n>Inspired by bytemporal scene graphs, we encode video sequences as sets of relation triplets in the form of (subjectit-predicate-object) over time via their language embeddings.<n>We evaluate the proposed framework on five challenging benchmarks: NeXT-QA, Intent-QA, STAR, VLEP, and TVQA.
• Score: 14.867263291053968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video-Question-Answering (VideoQA) comprises the capturing of complex visual relation changes over time, remaining a challenge even for advanced Video Language Models (VLM), i.a., because of the need to represent the visual content to a reasonably sized input for those models. To address this problem, we propose RElation-based Video rEpresentAtion Learning (REVEAL), a framework designed to capture visual relation information by encoding them into structured, decomposed representations. Specifically, inspired by spatiotemporal scene graphs, we propose to encode video sequences as sets of relation triplets in the form of (\textit{subject-predicate-object}) over time via their language embeddings. To this end, we extract explicit relations from video captions and introduce a Many-to-Many Noise Contrastive Estimation (MM-NCE) together with a Q-Former architecture to align an unordered set of video-derived queries with corresponding text-based relation descriptions. At inference, the resulting Q-former produces an efficient token representation that can serve as input to a VLM for VideoQA. We evaluate the proposed framework on five challenging benchmarks: NeXT-QA, Intent-QA, STAR, VLEP, and TVQA. It shows that the resulting query-based video representation is able to outperform global alignment-based CLS or patch token representations and achieves competitive results against state-of-the-art models, particularly on tasks requiring temporal reasoning and relation comprehension. The code and models will be publicly released.

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