ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos

• URL: http://arxiv.org/abs/2305.02519v1
• Date: Thu, 4 May 2023 03:04:59 GMT
• Title: ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos
• Authors: Zhou Yu, Lixiang Zheng, Zhou Zhao, Fei Wu, Jianping Fan, Kui Ren, Jun Yu
• Abstract summary: We present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over challenging untrimmed videos from ActivityNet. ANetQA attains 1.4 billion unbalanced and 13.4 million balanced QA pairs, which is an order of magnitude larger than AGQA with a similar number of videos. The best model achieves 44.5% accuracy while human performance tops out at 84.5%, leaving sufficient room for improvement.
• Score: 120.80589215132322
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compositional simple questions and suffer from language biases, making it difficult to diagnose model weaknesses incisively. A recent benchmark AGQA poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control. However, its questions have limitations in reasoning about the fine-grained semantics in videos as such information is absent in its scene graphs. To this end, we present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet. Similar to AGQA, the QA pairs in ANetQA are automatically generated from annotated video scene graphs. The fine-grained properties of ANetQA are reflected in the following: (i) untrimmed videos with fine-grained semantics; (ii) spatio-temporal scene graphs with fine-grained taxonomies; and (iii) diverse questions generated from fine-grained templates. ANetQA attains 1.4 billion unbalanced and 13.4 million balanced QA pairs, which is an order of magnitude larger than AGQA with a similar number of videos. Comprehensive experiments are performed for state-of-the-art methods. The best model achieves 44.5% accuracy while human performance tops out at 84.5%, leaving sufficient room for improvement.

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