The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
Open accessAbstract
Recent developments in modeling language and vision have been successfully applied to image question answering. It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA). Compared to the image domain where large scale and fully annotated benchmark datasets exists, VideoQA datasets are limited to small scale and are automatically generated, etc. These limitations restrict their applicability in practice. Here we introduce ActivityNet-QA, a fully annotated and large scale VideoQA dataset. The dataset consists of 58,000 QA pairs on 5,800 complex web videos derived from the popular ActivityNet dataset. We present a statistical analysis of our ActivityNet-QA dataset and conduct extensive experiments on it by comparing existing VideoQA baselines. Moreover, we explore various video representation strategies to improve VideoQA performance, especially for long videos.
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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 [130]
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 2×
“As we have obtained the sampled video unit set, for each unit ui, we used the VGG-16 network pre-trained on the ImageNet dataset (Simonyan and Zisserman 2014) to extract the appearance feature (the fc7 feature xi ∈ R(4096)) given the central frame of the unit, and the C3D network pretrained on the Sport-1M dataset (Tr…”
MethodHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 2×
“E-VQA is the extension of an ImageQA baseline (Antol et al. 2015), where one long-short term memory (LSTM) network (Hochreiter and Schmidhuber 1997) is used to encode all words in the question and another different LSTM network is used to encode the frames in the video.”
BackgroundMethodGlove: Global Vectors for Word Representation
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 1×
“All models use the pre-trained 300-dimensional GloVe embedding (Pennington, Socher, and Manning 2014) to initialize the question embedding layer.”
MethodLearning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · cited 2×
“As we have obtained the sampled video unit set, for each unit ui, we used the VGG-16 network pre-trained on the ImageNet dataset (Simonyan and Zisserman 2014) to extract the appearance feature (the fc7 feature xi ∈ R(4096)) given the central frame of the unit, and the C3D network pretrained on the Sport-1M dataset (Tr…”
MethodShow, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · 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/cvpr.2018.00636 · Cited by 5,416 · cited 1×
“…shown recent success, with many approaches proposed to investigate the key components of this task, e.g. , discriminative feature representation (Anderson et al. 2018), multi-modal fusion (Kim et al. 2017a; Yu et al. 2017; Yu et al. 2018a) and visual reasoning (Nam, Ha, and Kim 2016; Lu et al.…”
BackgroundLong-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · cited 2×
“These advances stimulated research into bridging the semantic connections between vision and language, such as in visual captioning (Donahue et al. 2015; Xu et al. 2015), visual grounding (Rohrbach et al. 2016; Chen, Kovvuri, and Nevatia 2017; Yu et al. 2018b) and visual question answering…”
BackgroundVisual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · cited 3×
“datasets, such as Visual Genome (Krishna et al. 2016) and VQA (Antol et al.”
BackgroundMethodCIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · cited 1×
“We evaluate the average similarities of each two natural language sentences using CIDEr score (Vedantam, Lawrence Zitnick, and Parikh 2015), setting an empirical threshold to the average CIDEr score, and manually checking samples that did not reach the threshold.”
MethodVQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Research works printed but not linked: 1
- Sukhbaatar, S.; Weston, J.; Fergus, R.; et al. 2015. End-toend memory networks. In NIPS , 2440-2448.
Cited by
Sorted by citations
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505
Self-citationVideoMamba: State Space Model for Efficient Video Understanding
Li, K., Li, X., Wang, Y., He, Y., Wang, Y., Wang, L., & Qiao, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_14 · Cited by 500
Span-based Localizing Network for Natural Language Video Localization
Zhang, H., Sun, A., Jing, W., & Zhou, J. T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.585 · Cited by 387
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions
Xiao, J., Shang, X., Yao, A., & Chua, T. (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/cvpr46437.2021.00965 · Cited by 391
“Unlike previous works [18, 56] which focus on reasoning temporal actions of a single object in a question, we emphasize more on object interactions.”
BackgroundMethodInfluentialMVBench: A Comprehensive Multi-modal Video Understanding Benchmark
Li, K., Wang, Y., He, Y., Li, Y., Wang, Y., Liu, Y., Wang, Z., Xu, J., Chen, G., Lou, P., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02095 · Cited by 300
“The evaluations show that, our VideoChat2 significantly surpasses the top-performing VideoChat [42] by over 15% accuracy on MVBench, and also achieves the new state-of-the-art results on video conversation [51] and zero-shot QA benchmarks [88, 98].”
BackgroundMethod
Metadata
- Authors
- Yu, Z., Xu, D., Yu, J., Yu, T., Zhao, Z., Zhuang, Y., & Tao, D.
- Affiliations
- Zhejiang University (CN)
- Hangzhou Dianzi University (CN)
- The University of Sydney (AU)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 9127-9134
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2964220823
- Cited by
- 281
- References
- 43
- Citations by year
- 2026: 44 · 2025: 73 · 2024: 47 · 2023: 39 · 2022: 26 · 2021: 32 · 2020: 15 · 2019: 5
- FWCI
- 6.36
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61602405, Grant 61622205, 61622205, 61702143, 61836002)
- China Knowledge Centre for Engineering Sciences and Technology
- Australian Research Council (FL170100117, IH180100002, DP180103424)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Keywords
- video question answering, annotated dataset, online video, ActivityNet, video representation
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