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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Citations
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
ActivityNet: A large-scale video benchmark for human activity understanding
Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.670 · Cited by 2,287
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547
Hierarchical Question-Image Co-Attention for Visual Question AnsweringPreprint
Lu, J., Yang, J., Batra, D., & Parikh, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00061 · Cited by 1,357 · cited 2×
“2017; 2018a) and visual reasoning (Nam, Ha, and Kim 2016; Lu et al. 2016; Johnson et al. 2017b).”
BackgroundDense-Captioning Events in Videos
Krishna, R., Hata, K., Ren, F., Fei-Fei, L., & Niebles, J. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.83 · Cited by 1,279
Describing Videos by Exploiting Temporal Structure
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.512 · Cited by 1,035 · cited 2×
“E-SA is the extension of the soft attention model (Yao et al. 2015) for ImageQA, where the question are fist encoded using a LSTM network.”
MethodMulti-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
Zhou, Y., Yu, J., Jianping Fan, B., & Tao, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.202 · Cited by 810
Self-citationDual Attention Networks for Multimodal Reasoning and Matching
Nam, H., Ha, J.-W., & Kim, J. (2017) · 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.2017.232 · Cited by 760 · cited 1×
“…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. 2016; Johnson et al. 2017b).”
BackgroundHadamard Product for Low-rank Bilinear PoolingPreprint
Kim, J.-H., On, K.-W., Lim, W., Kim, J., Ha, J.-W., & Zhang, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.04325 · Cited by 702
MovieQA: Understanding Stories in Movies through Question-Answering
Tapaswi, M., Zhu, Y., Stiefelhagen, R., Torralba, A. B., Urtasun, R., & Fidler, S. (2016) · 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.2016.501 · Cited by 646 · cited 3×
“Several VideoQA datasets have been compiled for different scenarios, such as MovieQA (Tapaswi et al. 2016), TGIF-QA (Jang et al.”
BackgroundMethodAsk Your Neurons: A Neural-Based Approach to Answering Questions about Images
Malinowski, M., Rohrbach, M., & Fritz, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.9 · Cited by 632 · cited 2×
“The WUPS score with the threshold γ is given by where w and v are the words in the each predicted answer and ground-truth answer respectively. µ γ ( w, v ) is given by otherwise (4) Following the setting in (Malinowski, Rohrbach, and Fritz 2015), we choose two thresholds γ = 0 .”
BackgroundMethodExploring Models and Data for Image Question AnsweringPreprint
Ren, M., Kiros, R., & Zemel, R. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02074 · Cited by 630 · cited 2×
“Referring to the taxonomy in existing VQA datasets (Ren, Kiros, and Zemel 2015; Antol et al. 2015), we manually categorize the samples into the following six classes by their answer types: Yes/No , Number , Color , Object , Location and Other .”
MethodVQA: Visual Question Answering: www.visualqa.org
Agrawal, A., Lu, J., Antol, S., Mitchell, M., Zitnick, C. L., Parikh, D., & Batra, D. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0966-6 · Cited by 570 · cited 11×
“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.”
BackgroundMethodResultInfluentialBeyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering
Zhou, Y., Yu, J., Xiang, C., Jianping Fan, B., & Tao, D. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2817340 · Cited by 543 · cited 2×
“…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. 2016; Johnson et al. 2017b).”
BackgroundSelf-citationTGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
Jang, Y., Song, Y., Yu, Y., Kim, Y., & Kim, G. (2017) · 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.2017.149 · Cited by 527 · cited 3×
“Existing approaches mainly focus on the temporal attention mechanism (Jang et al. 2017; Xu et al. 2017) or memory mechanism (Na et al. 2017; Kim et al. 2017b; Zhao et al. 2018a).”
BackgroundMethodInferring and Executing Programs for Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L. J. P., Hoffman, J., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.325 · Cited by 500 · cited 7×
“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.”
BackgroundResultInfluentialMaking the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., & Parikh, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1116-0 · Cited by 487 · cited 1×
“…and perform spatio-temporal reasoning on the visual contents of the video ( i.e. , frames in red border and objects in blue box). been facilitated by large scale and well annotated training datasets, such as Visual Genome (Krishna et al. 2016) and VQA (Antol et al. 2015; Goyal et al. 2017).”
BackgroundVideo Question Answering via Gradually Refined Attention over Appearance and Motion
Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., & Zhuang, Y. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123427 · Cited by 471 · cited 12×
“2017) MSVD Automatic OE 1,970 50,505 10s MSRVTT-QA (Xu et al. 2017) MSRVTT Automatic OE 10,000 243,680 15s TGIF-QA (Jang et al.”
BackgroundMethodInfluentialSelf-citationA Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain InputPreprint
Malinowski, M., & Fritz, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0210 · Cited by 432 · cited 6×
“WUPS is a generalization of the accuracy measure that accounts for word-level ambiguities in the answer words.”
MethodInfluentialGrounding of Textual Phrases in Images by Reconstruction
Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T., & Schiele, B. (2016) · Max Planck Digital Library · Cited by 422
Describing Videos by Exploiting Temporal StructurePreprint
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.08029 · Cited by 303 · cited 2×
“E-SA is the extension of the soft attention model (Yao et al. 2015) for ImageQA, where the question are fist encoded using a LSTM network.”
MethodFast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos
Heilbron, F. C., Niebles, J. C., & Ghanem, B. (2016) · 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.2016.211 · Cited by 279
Motion-Appearance Co-memory Networks for Video Question Answering
Gao, J., Ge, R., Chen, K., & Nevatia, R. (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.2018.00688 · Cited by 258
Ask Your Neurons: A Neural-based Approach to Answering Questions about ImagesPreprint
Malinowski, M., Rohrbach, M., & Fritz, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.01121 · Cited by 181 · cited 2×
“The WUPS score with the threshold γ is given by where w and v are the words in the each predicted answer and ground-truth answer respectively. µ γ ( w, v ) is given by otherwise (4) Following the setting in (Malinowski, Rohrbach, and Fritz 2015), we choose two thresholds γ = 0 .”
BackgroundMethodDeepStory: Video Story QA by Deep Embedded Memory Networks
Kim, K.-M., Heo, M.-O., Choi, S.-H., & Zhang, B. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/280 · Cited by 167
Leveraging Video Descriptions to Learn Video Question Answering
Zeng, K.-H., Chen, T.-H., Chuang, C.-Y., Liao, Y.-H., Niebles, J. C., & Sun, M. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v31i1.11238 · Cited by 162 · cited 3×
“2016) Movies Human MC 6,771 6,462 200s Video-QA (Zeng et al. 2017) Jukinmedia Automatic OE 18,100 174,775 45s”
MethodRethinking Diversified and Discriminative Proposal Generation for Visual Grounding
Zhou, Y., Yu, J., Xiang, C., Zhao, Z., Tian, Q. C., & Tao, D. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/155 · Cited by 143
Self-citationQuery-Guided Regression Network with Context Policy for Phrase Grounding
Chen, K., Kovvuri, R., & Nevatia, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.95 · Cited by 120 · cited 1×
“…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 (Malinowski, Rohrbach, and Fritz 2015; Fukui et al. 2016).”
BackgroundMulti-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question AnsweringPreprint
Zhou, Y., Yu, J., Fan, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.01471 · Cited by 101
Self-citationA Read-Write Memory Network for Movie Story Understanding
Na, S., Lee, S., Kim, J., & Kim, G. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.80 · Cited by 100 · cited 3×
“Existing approaches mainly focus on the temporal attention mechanism (Jang et al. 2017; Xu et al. 2017) or memory mechanism (Na et al. 2017; Kim et al. 2017b; Zhao et al. 2018a).”
BackgroundMethodOpen-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks
Zhao, Z., Zhang, Z., Xiao, S., Zhou, Y., Yu, J., Cai, D., Wu, F. Q., & Zhuang, Y. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/512 · Cited by 56
Self-citationMulti-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network
Zhao, Z., Jiang, X., Cai, D., Xiao, J., He, X., & Pu, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/513 · Cited by 43 · cited 2×
“Existing approaches mainly focus on the temporal attention mechanism (Jang et al. 2017; Xu et al. 2017) or memory mechanism (Na et al. 2017; Kim et al. 2017b; Zhao et al. 2018a).”
BackgroundMethodSelf-citationDeepStory: Video Story QA by Deep Embedded Memory NetworksPreprint
Kim, K.-M., Heo, M.-O., Choi, S.-H., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00836 · Cited by 33
Motion-Appearance Co-Memory Networks for Video Question AnsweringPreprint
Gao, J., Ge, R., Chen, K., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10906 · Cited by 23 · cited 2×
“proposed a co-memory network to jointly model and interact with the motion and appearance information (Gao et al. 2018).”
BackgroundMethodRethinking Diversified and Discriminative Proposal Generation for Visual GroundingPreprint
Zhou, Y., Yu, J., Xiang, C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.03508 · Cited by 3 · cited 3×
“2015), visual grounding (Rohrbach et al. 2016; Chen, Kovvuri, and Nevatia 2017; Yu et al. 2018b) and visual question answering (Malinowski, Rohrbach, and Fritz 2015; Fukui et al.”
BackgroundSelf-citation
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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