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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
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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×
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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×
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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×
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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×
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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
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Sorted by citations
Items 101–200 of 282
Dynamic self-attention with vision synchronization networks for video question answering
Liu, Y., Zhang, X., Huang, F., Shen, S., Tian, P., Li, L., & Li, Z. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108959 · Cited by 13
FunQA: Towards Surprising Video Comprehension
Xie, B., Zhang, S., Zhou, Z., Li, B., Zhang, Y., Hessel, J., Yang, J., & Liu, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73232-4_3 · Cited by 13
TG-VQA: Ternary Game of Video Question Answering
Li, H., Jin, P., Cheng, Z., Zhang, S., Chen, K., Wang, Z., Liu, C., & Chen, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/116 · Cited by 13
TVENet: Temporal variance embedding network for fine-grained action representation
Han, T., Yao, H., Xie, W., Sun, X., Zhao, S., & Yu, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107267 · Cited by 13
Self-citationMotion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
Deng, A., Chen, T., Yu, S., Yang, T., Spencer, L., Tian, Y., Mian, A. S., Bansal, M., & Chen, C. (2025) · 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/cvpr52734.2025.00806 · Cited by 18
Connecting Vision and Language with Video Localized Narratives
Voigtlaender, P., Changpinyo, S., Pont-Tuset, J., Soricut, R., & Ferrari, V. (2023) · 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/cvpr52729.2023.00243 · Cited by 12
“We evaluate the text answer predicted by a VideoQA method using the exact match accuracy against the ground-truth [61,64].”
MethodWang, H., Lai, C., Sun, Y., & Ge, W. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680826 · Cited by 15
“Early VideoQA benchmarks [16, 44, 53] focus on descriptive questions (e.g., [ what’s the man doing ]) within short video clips, rarely going beyond a recognition of the objects and actions.”
BackgroundVideo Question Answering with Phrases via Semantic Roles
Sadhu, A., Chen, K., & Nevatia, R. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.196 · Cited by 16
ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos
Zhou, Y., Zheng, L., Zhao, Z., Wu, F. Q., Jianping Fan, B., Ren, K., & Yu, J. (2023) · 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/cvpr52729.2023.02221 · Cited by 13
“VideoQA benchmarks can be categorized into two groups based on whether their videos are synthesized by simulation [29, 42] or collected from the real world [8, 17, 21, 28, 33, 36, 38, 39, 43, 46, 48].”
BackgroundMethodInfluentialSelf-citationLearning Situation Hyper-Graphs for Video Question Answering
Khan, A. U., Kuehne, H., Wu, B., Chheu, K., Bousselham, W., Gan, C., Lobo, N. D. V., & Shah, M. A. (2023) · 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/cvpr52729.2023.01429 · Cited by 11
“Several video QA benchmarks are introduced to evaluate this task from varying perspectives entailing description [34, 51, 57, 61], temporal reasoning [17, 53, 64], causal structures [48, 53], visual-language comprehension [26, 46, 52], relational reasoning [46], and measuring social intelligence [58].”
BackgroundForged Video Detection Using Deep Learning: A SLR
Munawar, M., Noreen, I., Alharthi, R. S., & Sarwar, N. (2023) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2023/6661192 · Cited by 12
Toward Long Video Understanding via Fine-Detailed Video Story Generation
You, Z., Wen, Z., Chen, Y., Li, X., Zeng, R., Wang, Y., & Tan, M. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3514820 · Cited by 11
“ActivityNet-QA [61] is derived from ActivityNet dataset [64].”
Do Language Models Understand Time?
Xi, D., & Wang, L. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717744 · Cited by 23
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
Li, S., Li, L., Liu, Y., Ren, S., Liu, Y., Gao, R., Sun, X., & Hou, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72897-6_19 · Cited by 29
Shuvo, M. R., Mekala, M. S., & Elyan, E. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10513-2 · Cited by 12
“For Temporal Action Localization (TAL), THU-MOS’14 [187] is the classic benchmark, while Activi-tyNet [188] provides a larger-scale challenge with longer, untrimmed videos.”
NewsKVQA: Knowledge-Aware News Video Question Answering
Gupta, P., & Gupta, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05981-0_1 · Cited by 11
VideoABC: A Real-World Video Dataset for Abductive Visual Reasoning
Zhao, W., Rao, Y., Tang, Y., Zhou, J., & Lu, J. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3205207 · Cited by 12
“This line of work is also extended to video-based QA [17], [18], [28].”
BackgroundEfficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer
Peng, M., Wang, C., Shi, Y., & Zhou, X.-D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25296 · Cited by 14
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
Lin, Y., Wei, C., Wang, H., Yuille, A., & Xie, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00233 · Cited by 11
“• ActivityNet-QA [65] collects 5.8K videos from Activ-ityNet [5] with 58K open-ended questions.”
BackgroundWatching the News: Towards VideoQA Models that can Read
Jahagirdar, S., Mathew, M., Karatzas, D., & Jawahar, C. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00442 · Cited by 12
“More recent works in VideoQA [17, 28, 34, 40] require models to reason about the events taking place in videos, but disregard any textual information in the videos.”
BackgroundInfluentialStep Differences in Instructional Video
Nagarajan, T., & Torresani, L. (2024) · 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/cvpr52733.2024.01773 · Cited by 9
“…video datasets [6, 9, 41, 45, 48, 59, 68, 69] have facilitated research in step captioning [63, 68], step detection [6, 45, 46, 48, 69], temporal grounding [2, 7, 13, 18, 28], vision-language representation learning [3, 25, 38, 66] and video question answering [56, 58, 60, 62] to name a few.”
BackgroundSiamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding
Tan, C., Lai, J., Zheng, W., & Hu, J. (2024) · 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/cvpr52733.2024.01288 · Cited by 11
“…which has received increasing attention due to its wide range of real-world applications such as video retrieval [3, 17, 21, 23, 80, 94], video summarization [50, 55, 59, 60, 83, 99], action segmentation [19, 27, 35, 37, 45, 72], video question answering [30, 36, 38, 41, 82, 89], etc.”
Wu, M., Zhao, C., Su, A., Di, D., Fu, T., An, D., He, M., Gao, Y., Ma, M., Yan, K., et al. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680810 · Cited by 11
“Moreover, these models can deduce the underlying implicit content of these explicit factors, leveraging the knowledge and experience acquired by LLMs. Existing benchmarks for video content question-and-answering, such as [30, 50, 72, 72, 76], provide a rich set of instruction labels.”
BackgroundGoldfish: Vision-Language Understanding of Arbitrarily Long Videos
Ataallah, K., Shen, X., Abdelrahman, E., Sleiman, E., Zhuge, M., Ding, J., Zhu, D., Schmidhuber, J., & Elhoseiny, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73397-0_15 · Cited by 35
Instruction-guided fusion of multi-layer visual features in Large Vision-Language Models
Xu, L., Zheng, Y., Chen, H., Chen, X., Liang, Y., Lai, C., Li, B., & Xue, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111932 · Cited by 10
Intra- and Inter-modal Multilinear Pooling with Multitask Learning for Video Grounding
Zhou, Y., Song, Y., Yu, J., Wang, M., & Huang, Q. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10205-y · Cited by 9
“To solve this problem, interactive video understanding tasks like video question answering [32,38] and video grounding are proposed.”
Self-citationTask-Oriented Multi-Modal Question Answering For Collaborative Applications
Tan, H. L., Leong, M. C., Xu, Q., Li, L., Fang, F., Cheng, Y., Gauthier, N., Sun, Y., & Lim, J. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190659 · Cited by 9
“Similarly, various video QA datasets such as MovieQA [2], TGIF-QA [11], TVQA+ [3], and ActivityNet-QA [4] have been proposed.”
BackgroundMethodInfluentialCRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions
Ates, T., Atesoglu, M. S., Yiğit, Ç., Kesen, İ., Kobaş, M., Erdem, E., Erdem, A., Göksun, T., & Yüret, D. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.205 · Cited by 14
SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge
Wang, A., Wu, B., Chen, S., Chen, Z., Guan, H., Lee, W.-N., Li, L. E., & Gan, C. (2024) · 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/cvpr52733.2024.01271 · Cited by 13
“For the final stage, these graphs aid in the creation of question templates, either through manual construction or by directing the LLM to generate question-answer pairs (QAs).”
BackgroundRanking Distillation for Open-Ended Video Question Answering with Insufficient Labels
Liang, T., Tan, C., Xia, B., Zheng, W., & Hu, J. (2024) · 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/cvpr52733.2024.01250 · Cited by 8
“50, 52, 59], which requires the model to find the correct answers in a large vocabulary (with over 1K possible answers) in response to the question regarding a given video.”
BackgroundMethodElysium: Exploring Object-Level Perception in Videos via MLLM
Wang, H., Ye, Y., Wang, Y., Nie, Y., & Huang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72670-5_10 · Cited by 18
Rethinking Temporal Context in Video-QA: A Comprehensive Study of Single-Frame Static Bias
Liang, T., Li, L., Hu, J., Yu, X., Zheng, W., & Lai, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543031 · Cited by 8
“Afterwards, numerous benchmarks [17], [18], [19], [20], [21] with manual annotations are designed to target the temporal, dynamic and causal cues.”
Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation
Shi, Y., Di, S., Chen, Q., & Xie, W. (2025) · 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/cvpr52734.2025.00797 · Cited by 8
“Video Question Answering (VideoQA) refers to a critical task that offers a natural interface for human-machine interaction through language [33, 42, 43, 49].”
Visual Instruction Tuning towards General-Purpose Multimodal Large Language Model: A Survey
Huang, J., Zhang, J., Jiang, K. Y., Qiu, H., Zhang, X., Shao, L., Lu, S., & Tao, D. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02572-7 · Cited by 44
Self-citationLanguage Models are Causal Knowledge Extractors for Zero-shot Video Question Answering
Su, H.-T., Niu, Y., Lin, X., Hsu, W. H., & Chang, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00523 · Cited by 8
“Subsequent VidQA datasets [16, 17, 40] stepped forward to temporal relations of successive events.”
BackgroundRelational Space-Time Query in Long-Form Videos
Yang, X., Chu, F.-J., Feiszli, M., Goyal, R., Torresani, L., & Tran, D. (2023) · 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/cvpr52729.2023.00619 · Cited by 8
GEM: A General Evaluation Benchmark for Multimodal Tasks
Su, L., Duan, N., Cui, E., Ji, L., Wu, C., Luo, H., Liu, Y., Zhong, M., Bharti, T., & Sacheti, A. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.229 · Cited by 15
Foundation Models for Video Understanding: A SurveyPreprint
Madan, N., Møgelmose, A., Modi, R., Rawat, Y. S., & Moeslund, T. B. (2024) · doi:10.36227/techrxiv.171769139.99464428/v2 · Cited by 11
GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations
Ilaslan, M. F., Song, C., Chen, J., Gao, D., Lei, W., Xu, Q., Lim, J., & Shou, M. Z. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.648 · Cited by 8
“…data, such as video clips, photos, and generated synthetic images, have been studied for a few decades and have achieved remarkable progress (Deng et al., 2009; He et al., 2016; Russell et al., 2008; Feichten-hofer et al., 2019; Tapaswi et al., 2016; Krizhevsky et al., 2017; Yu et al., 2019).”
BackgroundSentiment-Enhanced Graph-Based Sarcasm Explanation in Dialogue
Ouyang, K., Jing, L., Song, X., Liu, M., Hu, Y., & Nie, L. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3565959 · Cited by 11
“…the number of visual tokens by abstracting semantic-level visual concepts, leading to visual semantics deficiency (e.g., the loss of fine-grained attributes and spatial locality [53]), and causing the degradation of video comprehension capacity [54], [55].”
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning
Zhao, R., Yuan, Q., Li, J., Hu, H., Li, Y., Gao, Z., & Gao, F. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3621971 · Cited by 7
“Although many datasets follow the VQA format [11], [12], [13], they fail to capture the full complexity of AD due to the gap between traffic scenes and typical VQA content.”
Minerva: Evaluating Complex Video Reasoning
Nagrani, A., Menon, S., İşcen, A., Buch, S., Mehran, R., Jha, N., Hauth, A., Zhu, Y., Vondrick, C., Сиротенко, М., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02222 · Cited by 27
“Video benchmarks have evolved from testing action recognition on short clips [17, 26, 50] to more recent benchmarks testing advanced capabilities over longer time horizons (LVBench [45], VideoMME [15], Neptune [34], and others).”
Reading Between the Lanes: Text VideoQA on the Road
Tom, G., Mathew, M., Garcia-Bordils, S., Karatzas, D., & Jawahar, C. V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41731-3_9 · Cited by 17
“Synthetically generated datasets[42,46,7] contain questions that are generated through processing video descriptions, narration and template questions.”
BackgroundMethodAMEGO: Active Memory from Long EGOcentric Videos
Goletto, G., Nagarajan, T., Averta, G., & Damen, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72624-8_6 · Cited by 7
Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts
Engin, D., & Avrithis, Y. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00298 · Cited by 10
Ko, D., Lee, J. S., Choi, M., Chu, J., Park, J., & Kim, H. J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00288 · Cited by 8
“Specifically on ActivityNet-QA (Fig.”
BackgroundMethodInfluentialGuiding Audio-Visual Question Answering with Collective Question Reasoning
Pei, B., Huang, Y., Chen, G., Xu, J., Wang, L., Wang, L., Lü, T., Qiao, Y., & Wu, F. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02510-7 · Cited by 6
“To further validate the robustness of our model design, we apply our method to the more general VQA datasets, including MSRVTT-QA (Xu et al., 2017) , ActivityNet-QA (Yu et al., 2019) and MSVD-QA (Xu et al., 2017).”
OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts
Wang, Y., Wang, Y., Chen, B., Wu, T., Zhao, D., & Zheng, Z. (2025) · 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/cvpr52734.2025.01763 · Cited by 6
Yang, C., Dong, X., Zhu, X., Su, W., Wang, J., Tian, H., Chen, Z., Wang, W., Lu, L., & Dai, J. (2025) · 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/cvpr52734.2025.02322 · Cited by 16
“For video-language benchmarks, we use MVBench [22], VideoMME [13], MLVU [68], LongVideoBench [56], NextQA [57], Egoschema [34], Per-cepTest [40], and ActNet-QA [61].”
Kim, J., Kim, H., Lee, H., & Ro, Y. M. (2025) · 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/cvpr52734.2025.00318 · Cited by 6
“This suggests a future avenue for integrating a hybrid approach based on our framework by dynamically adjusting the complexity of the retrieval and processing mechanisms based on the video length and content density.”
PSAM: Parameter-Free Spatiotemporal Attention Mechanism for Video Question Answering
Zhang, F., Wang, R., Zhou, F., Luo, Y., & Li, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3333192 · Cited by 6
“ActivityNet-QA [55]: This is a dataset consisting of 58,000 question-answer pairs collected from 5,800 videos.”
Kim, M. J., Song, S., Lee, Y., Jang, H., & Lim, K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29798 · Cited by 7
“Additionally, VQA datasets have been proposed for domain-specific purposes, such as MovieQA (Tapaswi et al. 2016), TVQA (Lei et al. 2018), DramaQA (Choi et al. 2021), and ActivityNet-QA (Yu et al. 2019).”
MethodValley: Video Assistant with Large Language Model Enhanced Ability
Luo, R., Zhao, Z., Min, Y., Yang, Z., Qiu, M., Wei, Z., Wang, Y., & Chen, C. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3796716 · Cited by 271
“We evaluated Valley on six diverse datasets across two domains: (1) MSVD-QA [5], MSRVTT-QA [39], ActivityNet-QA [42], and Video-ChatGPT [22] for video understanding; (2) ScienceQA [21] and MemeCap [15] for image understanding.”
Self-citationQuestion-relationship guided graph attention network for visual question answer
Liu, R., Zhuang, L., Zhou, Y., Jiang, Z., & Bai, T. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00745-7 · Cited by 6
“One approach is to focus on better expression of the visual regions [8, 14, 16, 17, 22, 23, 33, 35].”
BackgroundRethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives
Xiao, S., Chen, L., Gao, K., Wang, Z., Yang, Y., Zhang, Z., & Xiao, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.561 · Cited by 12
“However, conventional VideoQA benchmarks (Xu et al., 2017; Yu et al., 2019) are constituted by descriptive questions, e . g ., asking the number, color or single action of the video elements.”
BackgroundHimakunthala, V., Ouyang, A., Rose, D., He, R., Mei, A., Lu, Y., Sonar, C., Saxon, M., & Wang, W. Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.15 · Cited by 19
“These datasets also provide sim-plifications as textual summaries (Xu et al., 2016; Guadarrama et al., 2013) or a single video frame (Yu et al., 2019; Zeng et al., 2017; Maharaj et al., 2017), which by themselves can be sufficient to complete the task.”
BackgroundYTCommentQA: Video Question Answerability in Instructional Videos
Yang, S., Park, S., Jang, Y., & Lee, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29906 · Cited by 11
Optimization of Artificial Neural Network: A Bat Algorithm-Based Approach
Gupta, T. K., & Raza, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96308-8_26 · Cited by 6
CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation LearningPreprint
Zhou, L., Liu, J., Yu, C., Gan, Z., & Zhang, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00285 · Cited by 8
“Depending on the duration of the video, VideoQA could target on short videos such as GIFs ( e.g ., TGIF-QA [28]), longer video clips ( e.g ., MSRVTT-QA [66] and MovieQA [63]), or an entire YouTube video ( e.g ., ActivityNet-QA [70]).”
BackgroundVideo Question Answering: A survey of the state-of-the-art
Jeshmol, P. J., & Kovoor, B. C. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104320 · Cited by 9
AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant
Lei, W., Gao, D., Wang, Y., Mao, D., Liang, Z., Ran, L., & Shou, M. Z. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.24 · Cited by 5
“Ad-ditionally, Video Question Answering (Lei et al., 2018; Tapaswi et al., 2016; Kim et al., 2017), with naturally occurring subtitles are particularly interesting, as it combines both visual and textual information for question answering.”
BackgroundT2V2T: Text-to-Video-to-Text Fusion for Text-to-Video Retrieval
Kim, J., Lee, Y., & Moon, J. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00594 · Cited by 5
“Recently, text-to-video retrieval methods have leveraged video-language pre-training (VLP) [2, 5, 7, 9, 12, 17, 24–26, 33] since VLP has shown superior performance in downstream tasks such as text-to-video retrieval [1,11,28], video question answering [14, 27, 30, 32], and video captioning [11, 28, 34].”
MethodQuestion-Answering Dense Video Events
Qin, H., Xiao, J., & Yao, A. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729945 · Cited by 11
“Video Question Answering VideoQA works are center on single event videos; this is reflected in the popular benchmarks, such as TGIF-QA (Jang et al. 2017), MSRVTT-QA and MSVD-QA (Xu et al. 2017), ActitivityNet-QA (Yu et al. 2019) and iVQA (Yang et al. 2021), and related techniques (Dai et al. 2023; Maaz et al. 2023; Zh…”
AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding
Lin, F., Tian, Y., Zhang, T., Huang, J., Guan, S., & Wang, F. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342598 · Cited by 5
“Following the construction methodology of ActivityNet-QA [22], question-answer pairs were designed accordingly.”
HySTER: A Hybrid Spatio-Temporal Event ReasonerPreprint
Sautory, T., Cingillioglu, N., & Russo, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06644 · Cited by 5
“The task of Video Question Answering (VideoQA) consists of the following: given a video alongside a question in natural language a model must return an answer in natural language (Yu et al. 2019).”
InfluentialStreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition
Ding, X., Wu, H. W., Yang, Y., Jiang, S., Zhang, Q., Bai, D., Chen, Z., & Cao, T. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01249 · Cited by 6
A Survey on Fine-Grained Multimodal Large Language Models
Peng, Y., Wang, Z., Li, G., Zheng, X., Yin, S., & He, H. (2026) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.336 · Cited by 10
Ma, Y., Liu, J., & Zhao, Y. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10377-7 · Cited by 4
“In recent years, pattern classification have been witnessed an increase in the use of various research domains, such as image, video analysis, computer vision [1,2], detect recognition and so on [3].”
BackgroundSpatio-Temporal Learning for Video Deblurring based on Two-Stream Generative Adversarial Network
Song, L., Wang, Q., Li, H., Fan, J., & Hu, B. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10520-y · Cited by 4
“Video deblurring has the great significance because improving video quality is helpful for video detecting [15], video question answering [48,50], visual representation for image captioning [49] and other applications.”
BackgroundVideo Entailment via Reaching a Structure-Aware Cross-modal Consensus
Yao, X., Gao, J., Chen, M., & Xu, C. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612345 · Cited by 4
“Recently, several video question answering (VideoQA) models [2, 56, 62] have been used to measure progress in understanding the interaction between video and language modalities.”
BackgroundMethodQiu, Z., McGill, M., Pöhlmann, K. M. T., & Brewster, S. A. (2024) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3641308.3685023 · Cited by 4
“Task performance involved participants watching six two-minute video clips and answering four video-related questions per clip.”
Collaborative Aware Bidirectional Semantic Reasoning for Video Question Answering
Wu, X., Wu, J., Zhu, L., Senhadji, L., & Shu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3490665 · Cited by 7
“ActivityNet-QA [53] is a long-form dataset for evaluating VideoQA models reasoning about complex spatial-temporal actions.”
Data Augmentation Techniques for the Video Question Answering Task
Falcon, A., Lanz, O., & Serra, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66415-2_33 · Cited by 6
Araujo, V., Soto, Á., & Moens, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.830 · Cited by 4
VQASTO: Visual Question Answering System for Action Surveillance based on Task Ontology
Vo, H. Q., Phung, T.-H., & Ly, N. Q. (2020) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics51282.2020.9335891 · Cited by 4
“Moreover, research on VideoQA (ActivityNet-QA [4], etc.”
Watch, Listen, and Answer: Open-Ended VideoQA with Modulated Multi-Stream 3D ConvNets
Miyanishi, T., & Kawanabe, M. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616257 · Cited by 4
Growing a Twig to Accelerate Large Vision-Language Models
Shao, Z., Wang, M., Yu, Z., Pan, W., Yang, Y., Wei, T., Zhang, H., Mao, N., Chen, W., & Yu, J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01866 · Cited by 3
“ActivityNet-QA [23] offers 58K human-annotated question-answer pairs across 5.8K videos from the ActivityNet dataset.”
Self-citationUnderstanding Video Scenes through Text: Insights from Text-based Video Question Answering
Jahagirdar, S., Mathew, M., Karatzas, D., & Jawahar, C. V. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00500 · Cited by 3
“Researchers have developed various datasets and methods to facilitate research in this field [8, 10, 13, 17, 19–21].”
BackgroundMethodHierarchical Banzhaf Interaction for General Video-Language Representation Learning
Jin, P., Li, H., Yuan, L., Yan, S., & Chen, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3522124 · Cited by 3
“We conduct comprehensive experiments on multiple widely used datasets, including three text-video retrieval datasets ( MSRVTT [13], Ac-tivityNet Captions [14], DiDeMo [15]), three VideoQA datasets ( MSRVTT-QA [16], MSVD-QA [16], ActivityNet-QA [17]), and one video captioning dataset ( MSRVTT [13]).”
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model
Xu, L., Zhu, S., Li, C., Kuo, C.-W., Chen, F., Wang, X., Chen, G., Du, D., Yuan, Y., & Wen, L. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00055 · Cited by 8
“Predominant evaluation protocols for video LMMs are still based on GPT judge and video VQA benchmarks, e.g., VideoChatGPT [25], ActivityNet-QA [37], MSVD-QA [36], MSRVTT-QA [36], TGIF-QA [15], which do not cover the emerging edited videos on social media.”
TrUMAn: Trope Understanding in Movies and Animations
Su, H.-T., Shen, P.-W., Tsai, B., Cheng, W., Wang, K.-J., & Hsu, W. H. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482018 · Cited by 4
“[22] proposed a human-annotated Video QA dataset, Activitynet-QA (Anet-QA), and extended the question types to include color, location, and spatial and temporal relations.”
BackgroundA Multi -level Alignment Training Scheme for Video-and-Language Grounding
Zhang, Y., Niu, F., Ping, Q., & Thattai, G. (2022) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw58026.2022.00124 · Cited by 3
“The ActivityNet-QA dataset [45] contains 58k QA pairs that come from 5.”
MethodResultMaintaining Common Ground in Dynamic Environments
Udagawa, T., & Aizawa, A. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00409 · Cited by 2
ATM: Action Temporality Modeling for Video Question Answering
Chen, J., Zhu, J., & Kong, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612509 · Cited by 2
“Escalating ImageQA [2], VideoQA [29, 31, 34, 52, 57, 62] is enriched with reasoning about temporal nature.”
BackgroundHSSHG: Heuristic Semantics-Constrained Spatio-Temporal Heterogeneous Graph for VideoQA
Wang, R., Luo, Y., Zhang, F., Liu, M., & Luo, X. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3443661 · Cited by 3
“E-SA [25]: An extension of the ImageQA soft attention model to focus on features of video features using question features encoded by the LSTM network.”
InfluentialQCaption: Video Captioning and Q&A through Fusion of Large Multimodal Models
Wang, J., Ng, G. W., Mak, L. O., Cher, R., Ryan, N. D. H., & Wang, D. (2024) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion59988.2024.10706514 · Cited by 3
“…work like Video-LLaVA [10], we employed three commonly reported datasets: YouCook2 [25] and MSR-VTT [26] for video captioning, and ActivityNet-QA [27] for video Q&A. YouCook2 comprises untrimmed, third-person viewpoint videos from 89 global cooking recipes, each annotated with procedural steps…”
CASP: Compression of Large Multimodal Models Based on Attention Sparsity
Gholami, M., Akbari, M., Cannons, K. J., & Zhang, Y. (2025) · 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/cvpr52734.2025.00875 · Cited by 3
“ActivityNet [22] CE-VQA Accuracy/ GPT-Assisted score Answer the question using a single word or phrase.”
REEF: Relevance-Aware and Efficient LLM Adapter for Video Understanding
Reza, S., Song, X., Yu, H., Lin, Z., Moghaddam, M., & Camps, O. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00244 · Cited by 2
Enhancing Long Video Understanding via Hierarchical Event-Based Memory
Cheng, D., Li, M., Liu, J., Guo, Y., Jiang, B., Liu, Q., Chen, X., & Zhao, B. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210102 · Cited by 2
“ActivityNet-QA [27] is a large-scale dataset focused on human activities, with its videos having an average duration of 3 minutes.”
STORM: Token-Efficient Long Video Understanding for Multimodal LLMs
Jiang, J., Li, X., Liu, Z., Li, M., Chen, G., Li, Z., Huang, D.-A., Liu, G., Yu, Z., Keutzer, K., et al. (2025) · 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00614 · Cited by 2
“LLaVA-SFT [1], Idefics2-SFT [55] MSR-VTT [58], Image Paragraph Captioning [59], ShareGPT4V-100K [60] CLEVR [61], NLVR, VisualMRC [62] ActivityNet-QA [63], LLaVA-OneVision-Images-SFT [45], iVQA [64], MSRVTT-QA, STEM-QA [65] DVQA [66], ST-VQA [67], SynthDoG-en [68], TextOCR-GPT4V [69], MTWI…”
A Large-Scale Multimodal Dataset and Benchmarks for Human Activity Scene Understanding and Reasoning
Jiang, S., Yuan, M., Ji, X., Yang, B., Liu, Z., Xu, L., Li, Y. X., He, Y., Dong, L., Lu, W., et al. (2026) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3745756.3809209 · Cited by 8
QAVidCap: Enhancing Video Captioning through Question Answering Techniques
Liu, H. L., & Wan, X. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3658061 · Cited by 2
PiTe: Pixel-Temporal Alignment for Large Video-Language Model
Liu, Y., Ding, P., Huang, S., Zhang, M., Zhao, H., & Wang, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_10 · Cited by 4
Dam: Dynamic Adapter Merging for Continual Video QA Learning
Cheng, F., Wang, Z., Sung, Y.-L., Lin, Y.-B., Bansal, M., & Bertasius, G. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00662 · Cited by 2
“Our findings demonstrate that our proposed D A M scheme outperforms the leading DIL method, S-Prompts, by a substantial margin of 9.1% in average accuracy while also exhibiting 1.9% less forgetting.”
InfluentialPerceive. Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries
Amoroso, R., Zhang, G., Koner, R., Baraldi, L., Cucchiara, R., & Volker Tresp, C. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00858 · Cited by 2
“The development of video QA has been driven by diverse benchmark datasets spanning multiple domains, including movies and TV shows [17,18, 32,40], as well as wild, online videos [10,37,38,40,48].”
LvBench: A Benchmark for Long-form Video Understanding with Versatile Multi-modal Question Answering
Zhang, H., Dong, L., Liu, Y., Huang, Y., Wang, L., Wang, L., & Qiao, Y. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02555-8 · Cited by 48
“…only requires capturing clues from a long temporal span but also requires high-level processing of these relevant clues. benchmarks fall short in genuinely addressing long video understanding [4, 67, 59, 53], primarily due to their limitations in two crucial aspects: clue length and video length.”
LOVO: Efficient Complex Object Query in Large-Scale Video Datasets
Liu, Y., Peng, Y., Zhou, H., Liu, H., Lu, X., Lou, J., Wu, C., Zhao, W., & Li, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00148 · Cited by 2
“To further demonstrate LOVO’s robustness and generalizability in handling diverse queries, we randomly selected 12 videos from ActivityNet-QA dataset [51].”
Spatial–Temporal Clue Reasoning Chain for Long Video Question Answering
Gong, H., Yan, C., Zhang, J., Sun, Y., Gao, Y., & Li, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3609441 · Cited by 4
“These QA pairs are sampled from three scenario-specific datasets: AGQA v2 (indoor), ActivityNet-QA [48] (outdoor), and EgoTaskQA (egocentric), which contribute approximately 9k, 5k, and 2k videos, respectively.”
Locating Visual Explanations for Video Question Answering
Chen, X., Liu, R. Z. Z., Song, X., & Han, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67832-6_24 · Cited by 2
Attend What You Need: Motion-Appearance Synergistic Networks for Video Question AnsweringPreprint
Seo, A., Kang, G.-C., Park, J., & Zhang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10446 · Cited by 10
“In particular, video question answering (video QA) tasks (Xu et al., 2016; Jang et al., 2017; Lei et al., 2018; Yu et al., 2019; Choi et al., 2020) have been proposed with the goal of reasoning over higher-level vision-”
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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