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Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
2009
Earlier work this paper cites.
Chen, D.L., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: ACL (2011)
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
Das, P., Xu, C., Doell, R.F., Corso, J.J.: A thousand frames in just a few words: Lingual description of videos through latent topics and sparse object stitching. In: CVPR (2013)
2013
Earlier work this paper cites.
Kuehne, H., Arslan, A., Serre, T.: The language of actions: Recovering the syntax and semantics of goal-directed human activities. In: CVPR (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. NeurIPS (2014)
2014
Earlier work this paper cites.
Heilbron, F.C., Escorcia, V., Ghanem, B., Niebles, J.C.: Activitynet: A large-scale video benchmark for human activity understanding. In: CVPR (2015)
2015
Earlier work this paper cites.
Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: IEEE International Conference on Computer Vision (2015)
2015
Earlier work this paper cites.
Rohrbach, A., Torabi, A., Rohrbach, M., Tandon, N., Pal, C.J., Larochelle, H., Courville, A.C., Schiele, B.: Movie description. International Journal of Computer Vision (2016)
2016
Earlier work this paper cites.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Gool, L.V.: Temporal segment networks: Towards good practices for deep action recognition. In: ECCV (2016)
2016
Earlier work this paper cites.
Xu, J., Mei, T., Yao, T., Rui, Y.: Msr-vtt: A large video description dataset for bridging video and language. In: CVPR (2016)
2016
Earlier work this paper cites.
Anne Hendricks, L., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.: Localizing moments in video with natural language. In: ICCV (2017)
2017
Earlier work this paper cites.
Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: CVPR (2017)
2017
Earlier work this paper cites.
Gao, J., Sun, C., Yang, Z., Nevatia, R.: Tall: Temporal activity localization via language query. In: ICCV (2017)
2017
Earlier work this paper cites.
Goyal, R., Kahou, S.E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fründ, I., Yianilos, P., Mueller-Freitag, M., Hoppe, F., Thurau, C., Bax, I., Memisevic, R.: The “something something” video database for learning and evaluating visual common sense. In: ICCV (2017)
2017
Earlier work this paper cites.
Gu, C., Sun, C., Vijayanarasimhan, S., Pantofaru, C., Ross, D.A., Toderici, G., Li, Y., Ricco, S., Sukthankar, R., Schmid, C., Malik, J.: Ava: A video dataset of spatio-temporally localized atomic visual actions. CVPR (2017)
2017
Earlier work this paper cites.
Idrees, H., Zamir, A.R., Jiang, Y.G., Gorban, A., Laptev, I., Sukthankar, R., Shah, M.: The thumos challenge on action recognition for videos “in the wild”. Computer Vision and Image Understanding (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Sharma, P., Ding, N., Goodman, S., Soricut, R.: Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In: ACL (2018)
2018
Earlier work this paper cites.
Tran, D., xiu Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: CVPR (2018)
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: ICCV (2019)
2019
Earlier work this paper cites.
Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: ICCV (2019)
2019
Earlier work this paper cites.
Hussein, N., Gavves, E., Smeulders, A.W.M.: Timeception for complex action recognition. In: CVPR (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS (2019)
2019
Earlier work this paper cites.
Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In: ICCV (2019)
2019
Earlier work this paper cites.
Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: Videobert: A joint model for video and language representation learning. In: ICCV (2019)
2019
Earlier work this paper cites.
Tang, Y., Ding, D., Rao, Y., Zheng, Y., Zhang, D., Zhao, L., Lu, J., Zhou, J.: Coin: A large-scale dataset for comprehensive instructional video analysis. In: CVPR (2019)
2019
Earlier work this paper cites.
Yu, Z., Xu, D., Yu, J., Yu, T., Zhao, Z., Zhuang, Y., Tao, D.: Activitynet-qa: A dataset for understanding complex web videos via question answering. In: AAAI (2019)
2019
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: NeurIPS (2020)
2020
Cited alongside, same era.
Feichtenhofer, C.: X3d: Expanding architectures for efficient video recognition. In: CVPR (2020)
2020
Cited alongside, same era.
Li, K., Li, X., Wang, Y., Wang, J., Qiao, Y.: Ct-net: Channel tensorization network for video classification. In: ICLR (2020)
2020
Cited alongside, same era.
Shao, D., Zhao, Y., Dai, B., Lin, D.: Finegym: A hierarchical video dataset for fine-grained action understanding. CVPR (2020)
2020
Li, K., Wang, Y., Peng, G., Song, G., Liu, Y., Li, H., Qiao, Y.: Uniformer: Unified transformer for efficient spatial-temporal representation learning. In: ICLR (2022)
2022
Later among the works it cites.
Lin, X., Petroni, F., Bertasius, G., Rohrbach, M., Chang, S.F., Torresani, L.: Learning to recognize procedural activities with distant supervision. CVPR (2022)
2022
Later among the works it cites.
Liu, Y., Wang, L., Wang, Y., Ma, X., Qiao, Y.: Fineaction: A fine-grained video dataset for temporal action localization. TIP (2022)
2022
Later among the works it cites.
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H.: Video swin transformer. In: CVPR (2022)
2022
Later among the works it cites.
Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: CVPR (2022)
2022
Later among the works it cites.
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Cited alongside, same era.
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: Vivit: A video vision transformer. In: ICCV (2021)
2021
Cited alongside, same era.
Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: A joint video and image encoder for end-to-end retrieval. In: ICCV (2021)
2021
Cited alongside, same era.
Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML (2021)
2021
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2021
Cited alongside, same era.
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., Feichtenhofer, C.: Multiscale vision transformers. In: ICCV (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: Vision and language representation learning with momentum distillation. In: NeurIPS (2021)
2021
Cited alongside, same era.
Luo, H., Ji, L., Zhong, M., Chen, Y., Lei, W., Duan, N., Li, T.: Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. Neurocomputing (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Nguyen, E., Goel, K., Gu, A., Downs, G.W., Shah, P., Dao, T., Baccus, S.A., Ré, C.: S4nd: Modeling images and videos as multidimensional signals with state spaces. In: NeurIPS (2022)
2022
Later among the works it cites.
Tong, Z., Song, Y., Wang, J., Wang, L.: VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training. In: NeurIPS (2022)
2022
Later among the works it cites.
Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.G., Zhou, L., Yuan, L.: Bevt: Bert pretraining of video transformers. CVPR (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Zhang, D.J., Li, K., Wang, Y., Chen, Y., Chandra, S., Qiao, Y., Liu, L., Shou, M.Z.: Morphmlp: An efficient mlp-like backbone for spatial-temporal representation learning. In: ECCV (2022)
2022
Later among the works it cites.
Fu, D.Y., Dao, T., Saab, K.K., Thomas, A.W., Rudra, A., Ré, C.: Hungry hungry hippos: Towards language modeling with state space models. In: ICLR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Li, K., Wang, Y., Li, Y., Wang, Y., He, Y., Wang, L., Qiao, Y.: Unmasked teacher: Towards training-efficient video foundation models. In: ICCV (2023)
2023
Later among the works it cites.
Smith, J.T., Warrington, A., Linderman, S.W.: Simplified state space layers for sequence modeling. In: ICLR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Team, G.: Gemini: A family of highly capable multimodal models. ArXiv abs/2312.11805
2023
Later among the works it cites.
Team, R.: Rwkv: Reinventing rnns for the transformer era. In: EMNLP (2023)
2023
Later among the works it cites.
Wang, Y., He, Y., Li, Y., Li, K., Yu, J., Ma, X.J., Chen, X., Wang, Y., Luo, P., Liu, Z., Wang, Y., Wang, L., Qiao, Y.: Internvid: A large-scale video-text dataset for multimodal understanding and generation. ArXiv (2023)
2023
Later among the works it cites.
Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., Ramesh, A.: Video generation models as world simulators (2024), https://openai.com/research/video-generation-models-as-world-simulators
2024
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