The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
TVQA: Localized, Compositional Video Question Answering
Open accessAbstract
Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks.However, due to data limitations, there has been much less work on video-based QA.In this paper, we present TVQA, a largescale video QA dataset based on 6 popular TV shows.TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video.Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts.We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task.The dataset is publicly available at http://tvqa.cs.unc.edu.
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“Overall, they obtained promising results on the TVQA [129] and ActivityNetQA [130] datasets.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [129]
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Deep Residual Learning for Image Recognition
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“We also use ResNet101 (He et al., 2016) trained on ImageNet (Deng et al., 2009) to extract whole image features.”
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Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472 · cited 5×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundMethodInfluentialFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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“ImageNet Features: We extract the pooled 2048D feature of the last block of ResNet101.”
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Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · cited 4×
“A subtitle S, which contains a set of sentences, is flattened into a long sequence of words and GloVe (Pennington et al., 2014) is used to embed the words.”
BackgroundMethodInfluentialSQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · cited 2×
“With the same goal in mind, Rajpurkar et al. (2016) introduced the SQuAD dataset, but their answers are specific spans from long passages.”
BackgroundVQA: Visual Question Answering
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“Text Question Answering: The related task of text-based question answering has been extensively explored (Richardson et al., 2013; Weston et al., 2015; Rajpurkar et al., 2016; Hermann et al., 2015; Hill et al., 2015).”
BackgroundCLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
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Sorted by citations
From Recognition to Cognition: Visual Commonsense Reasoning
Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00688 · Cited by 769
“While past work has asked humans to write several counterfactual choices for each correct answer [75, 46], this process is expensive.”
BackgroundLess is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00725 · Cited by 639
“Popular video-andlanguage tasks include text-to-video retrieval [75, 28, 54], video captioning [75, 82, 28, 54, 40], video question answering [74, 23, 33], and moment retrieval [2, 18, 35].”
BackgroundInfluentialSelf-citationLearning 2D Temporal Adjacent Networks for Moment Localization with Natural Language
Zhang, S., Peng, H., Fu, J., & Luo, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6984 · Cited by 513
Zhao, F., Zhang, C., & Geng, B. (2024) · ACM Computing Surveys · doi:10.1145/3649447 · Cited by 480
A Corpus for Reasoning about Natural Language Grounded in Photographs
Suhr, A., Zhou, S., Zhang, A. Q., Zhang, I., Bai, H., & Artzi, Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1644 · Cited by 628
“, 2018) Visual Question Answering TVQA (Lei et al., 2018) Video Question Answering 4 4”
Background
Metadata
- Authors
- Lei, J., Yu, L., Bansal, M., & Berg, T.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 1369-1379
- OpenAlex
- W2963541336
- Cited by
- 541
- References
- 49
- Citations by year
- 2026: 39 · 2025: 50 · 2024: 65 · 2023: 93 · 2022: 90 · 2021: 101 · 2020: 70 · 2019: 32 · 2018: 1
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1405822, 1562098, 1633295)
- Army Research Office (W911NF)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- video question answering, video localization, television series, end-to-end learning
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