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We present MaCLR, a novel method to explicitly perform cross-modal self-supervised video representations learning from visual and motion modalities.
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: ECCV (2006)
2006
Earlier work this paper cites.
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR (2006)
2006
Earlier work this paper cites.
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)
2008
Earlier work this paper cites.
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.
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. T-PAMI (2011)
2011
Earlier work this paper cites.
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: ICCV (2011)
2011
Earlier work this paper cites.
Soomro, K., Zamir, A.R., Shah, M.: A dataset of 101 human action classes from videos in the wild. In: ICCV Workshops (2013)
2013
Earlier work this paper cites.
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: ICCV (2013)
2013
Earlier work this paper cites.
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: Large displacement optical flow with deep matching. In: ICCV (2013)
2013
Earlier work this paper cites.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. T-PAMI (2014)
2014
Earlier work this paper cites.
Sobel, I.: History and definition of the sobel operator (2014)
2014
Earlier work this paper cites.
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised Visual Representation Learning by Context Prediction. In: ICCV (2015)
2015
Earlier work this paper cites.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazrba, C., Golkov, V., Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: ICCV (2015)
2015
Earlier work this paper cites.
Fragkiadaki, K., Arbelaez, P., Felsen, P., Malik, J.: Learning to segment moving objects in videos. In: CVPR (2015)
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. In: ICLR (2015)
2015
Earlier work this paper cites.
Wang, X., Gupta, A.: Unsupervised Learning of Visual Representations using Videos. In: ICCV (2015)
2015
Earlier work this paper cites.
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional siamese networks for object tracking. In: ECCV (2016)
2016
Earlier work this paper cites.
Feichtenhofer, C., Pinz, A., Wildes, R.: Spatiotemporal residual networks for video action recognition. In: NeurIPS (2016)
2016
Earlier work this paper cites.
Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: ECCV (2016)
2016
Earlier work this paper cites.
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: ECCV (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Tsai, Y.H., Yang, M.H., Black, M.J.: Video segmentation via object flow. In: CVPR (2016)
2016
Earlier work this paper cites.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L.: Temporal segment networks: Towards good practices for deep action recognition. In: ECCV (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: ECCV (2016)
2016
Earlier work this paper cites.
Brattoli, B., Buchler, U., Wahl, A.S., Schwab, M.E., Ommer, B.: LSTM self-supervision for detailed behavior analysis. In: CVPR (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.
Feichtenhofer, C., Pinz, A., Zisserman, A.: Detect to track and track to detect. In: ICCV (2017)
2017
Earlier work this paper cites.
Fernando, B., Bilen, H., Gavves, E., Gould, S.: Self-supervised video representation learning with odd-one-out networks. In: CVPR (2017)
2017
Cited alongside, same era.
Kang, K., Li, H., Xiao, T., Ouyang, W., Yan, J., Liu, X., Wang, X.: Object detection in videos with tubelet proposal networks. In: CVPR (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Noroozi, M., Pirsiavash, H., Favaro, P.: Representation learning by learning to count. In: ICCV (2017)
2017
Cited alongside, same era.
Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR (2017)
Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. In: CVPR (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Alwassel, H., Mahajan, D., Korbar, B., Torresani, L., Ghanem, B., Tran, D.: Self-supervised learning by cross-modal audio-video clustering. In: NeurIPS (2020)
2020
Later among the works it cites.
Benaim, S., Ephrat, A., Lang, O., Mosseri, I., Freeman, W.T., Rubinstein, M., Irani, M., Dekel, T.: SpeedNet: Learning the speediness in videos. In: CVPR (2020)
2020
Later among the works it cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: NeurIPS (2020)
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2017
Cited alongside, same era.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: ICCV (2017)
2017
Cited alongside, same era.
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., Brox, T.: Demon: Depth and motion network for learning monocular stereo. In: CVPR (2017)
2017
Cited alongside, same era.
Zhu, X., Wang, Y., Dai, J., Yuan, L., Wei, Y.: Flow-guided feature aggregation for video object detection. ICCV (2017)
2017
Cited alongside, same era.
Bao, L., Wu, B., Liu, W.: CNN in MRF: Video object segmentation via inference in a CNN-based higher-order spatio-temporal MRF. In: CVPR (2018)
2018
Cited alongside, same era.
Buchler, U., Brattoli, B., Ommer, B.: Improving spatiotemporal self-supervision by deep reinforcement learning. In: ECCV (2018)
2018
Cited alongside, same era.
Cheng, J., Tsai, Y.H., Hung, W.C., Wang, S., Yang, M.H.: Fast and accurate online video object segmentation via tracking parts. In: CVPR (2018)
2018
Cited alongside, same era.
Gu, C., Sun, C., Ross, D.A., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., et al.: AVA: A video dataset of spatio-temporally localized atomic visual actions. In: CVPR (2018)
2018
Cited alongside, same era.
2020
Later among the works it cites.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: NeurIPS (2020)
2020
Later among the works it cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Fan, H., Li, Y., Xiong, B., Lo, W.Y., Feichtenhofer, C.: Pyslowfast. https://github.com/facebookresearch/slowfast (2020)
2020
Later among the works it cites.
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G., et al.: Bootstrap your own latent: A new approach to self-supervised learning. In: NeurIPS (2020)
2020
Later among the works it cites.
Han, T., Xie, W., Zisserman, A.: Memory-augmented dense predictive coding for video representation learning. In: ECCV (2020)
2020
Later among the works it cites.
Han, T., Xie, W., Zisserman, A.: Self-supervised co-training for video representation learning. In: NeurIPS (2020)
2020
Later among the works it cites.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)
2020
Later among the works it cites.
Miech, A., Alayrac, J.B., Smaira, L., Laptev, I., Sivic, J., Zisserman, A.: End-to-end learning of visual representations from uncurated instructional videos. In: CVPR (2020)
2020
Later among the works it cites.
Misra, I., Maaten, L.v.d.: Self-supervised learning of pretext-invariant representations. In: CVPR (2020)
2020
Later among the works it cites.
Piergiovanni, A., Angelova, A., Ryoo, M.S.: Evolving losses for unsupervised video representation learning. In: CVPR (2020)
2020
Later among the works it cites.
Teed, Z., Deng, J.: Raft: Recurrent all-pairs field transforms for optical flow. In: ECCV (2020)
2020
Later among the works it cites.
Chen, X., He, K.: Exploring simple siamese representation learning. In: CVPR (2021)
2021
Closest in time.
Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R., He, K.: A large-scale study on unsupervised spatiotemporal representation learning. In: CVPR (2021)
2021
Closest in time.
Gavrilyuk, K., Jain, M., Karmanov, I., Snoek, C.G.: Motion-augmented self-training for video recognition at smaller scale. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
2021
Closest in time.
Huang, D., Wu, W., Hu, W., Liu, X., He, D., Wu, Z., Wu, X., Tan, M., Ding, E.: ASCNet: Self-supervised video representation learning with appearance-speed consistency. In: ICCV (2021)
2021
Closest in time.
Huang, L., Liu, Y., Wang, B., Pan, P., Xu, Y., Jin, R.: Self-supervised video representation learning by context and motion decoupling. In: CVPR (2021)
2021
Closest in time.
Patrick, M., Asano, Y.M., Kuznetsova, P., Fong, R., Henriques, J.F., Zweig, G., Vedaldi, A.: Multi-modal self-supervision from generalized data transformations. In: ICCV (2021)
2021
Closest in time.
Qian, R., Li, Y., Liu, H., See, J., Ding, S., Liu, X., Li, D., Lin, W.: Enhancing self-supervised video representation learning via multi-level feature optimization. In: ICCV (2021)
2021
Closest in time.
Qian, R., Meng, T., Gong, B., Yang, M.H., Wang, H., Belongie, S., Cui, Y.: Spatiotemporal contrastive video representation learning. In: CVPR (2021)
2021
Closest in time.
Recasens, A., Luc, P., Alayrac, J.B., Wang, L., Strub, F., Tallec, C., Malinowski, M., Patraucean, V., Altché, F., Valko, M., et al.: Broaden your views for self-supervised video learning. In: ICCV (2021)
2021
Closest in time.
Sadhu, A., Gupta, T., Yatskar, M., Nevatia, R., Kembhavi, A.: Visual semantic role labeling for video understanding. In: CVPR (2021)
2021
Closest in time.