Unsupervised Learning of Visual Representations Using Videos

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

SURF: Speeded Up Robust Features

Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436

Spatiotemporal Contrastive Learning of Facial Expressions in Videos

Roy, S., & Etemad, A. (2021) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii52823.2021.9597460 · Cited by 18

Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks

Schreyer, M., Sattarov, T., & Borth, D. (2021) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3490354.3494373 · Cited by 28

IHEM Loss: Intra-Class Hard Example Mining Loss for Robust Face Recognition

Xiao, D., Li, J., Li, J., Dong, S., & Lü, T. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3184415 · Cited by 18

Unsupervised learning from videos using temporal coherency deep networks

Redondo-Cabrera, C., & López-Sastre, R. J. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.08.003 · Cited by 18

MSANet: multimodal self-augmentation and adversarial network for RGB-D object recognition

Zhou, F., Hu, Y., & Shen, X. (2018) · The Visual Computer · doi:10.1007/s00371-018-1559-x · Cited by 18

Generic Neural Architecture Search via RegressionPreprint

Li, Y., Hao, C., Li, P., Xiong, J., & Chen, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.01899 · Cited by 19

Knowledge Evolution in Neural Networks

Taha, A., Shrivastava, A., & Davis, L. S. (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/cvpr46437.2021.01265 · Cited by 19

CDFSL-V: Cross-Domain Few-Shot Learning for Videos

Samarasinghe, S., Rizve, M. N., Kardan, N., & Shah, M. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01069 · Cited by 17

Hard negative mining for correlation filters in visual tracking

Sun, Z., Wang, Y., & Laganière, R. (2019) · Machine Vision and Applications · doi:10.1007/s00138-019-01004-0 · Cited by 17

Beyond Supervised Learning: A Computer Vision Perspective

Chum, L., Subramanian, A., Balasubramanian, V. N., & Jawahar, C. V. (2019) · Journal of the Indian Institute of Science · doi:10.1007/s41745-019-0099-3 · Cited by 18

Improving Deep Binary Embedding Networks by Order-Aware Reweighting of Triplets

Lai, H., Chen, J., Geng, L., Pan, Y., Liang, X., & Yin, J. P. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2899055 · Cited by 17

CortexNet: a Generic Network Family for Robust Visual Temporal RepresentationsPreprint

Canziani, A., & Culurciello, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02735 · Cited by 17

Unsupervised Learning of Visual Representations by Solving Jigsaw PuzzlesPreprint

Noroozi, M., & Favaro, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.09246 · Cited by 78

Localizing and Orienting Street Views Using Overhead ImageryPreprint

Vo, N., & Hays, J. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.00161 · Cited by 17

Rethinking deep active learning: Using unlabeled data at model trainingPreprint

Siméoni, O., Budnik, M., Avrithis, Y., & Gravier, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08177 · Cited by 22

Self-Supervised Approach for Facial Movement Based Optical Flow

Alkaddour, M., Tariq, U., & Dhall, A. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3197622 · Cited by 16

User Selection and Power Allocation Scheme With SINR-Based Deep Learning for Downlink NOMA

Kim, D., Jung, H., & Lee, I.-H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3246982 · Cited by 16

Multimodal Composition Example Mining for Composed Query Image Retrieval

Zhang, G., Li, S., Wei, S., Ge, S., Cai, N., & Zhao, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3359062 · Cited by 17

Watch fashion shows to tell clothing attributes

Zhang, S., Liu, C., Cao, X., Song, Z., & Zhou, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.12.027 · Cited by 16

Unsupervised Discriminative Embedding For Sub-Action Learning in Complex Activities

Swetha, S., Kuehne, H., Rawat, Y. S., & Shah, M. A. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506759 · Cited by 15

Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting

Toering, M., Gatopoulos, I., Stol, M., & Hu, V. T. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00092 · Cited by 16

Contrastive attention network with dense field estimation for face completion

Ma, X., Zhou, X., Huang, H., Jia, G., Chai, Z., & Wei, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108465 · Cited by 16

Survey on clothing image retrieval with cross-domain

Chen, N., Di, Y., & Li, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00750-5 · Cited by 16

Time-based self-supervised learning for Wireless Capsule Endoscopy

Pascual, G., Laiz, P., García, A., Wenzek, H. R., Vitria, J. M., & Seguí, S. (2022) · Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) · Cited by 16

Attention driven multi-modal similarity learning

Gao, X., Mu, T., Goulermas, J. Y., & Wang, M. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.026 · Cited by 16

Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning From Unlabeled Videos and 3D-Shape Retrieval

Markus, N., Pandzic, I., & Ahlberg, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2867270 · Cited by 16

Position-based monocular visual servoing of an unknown target using online self-supervised learning

Lee, C., Seo, H., & Kim, H. J. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968216 · Cited by 15

Multiview Supervision By Registration

Zhang, Y., & Park, H. S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093591 · Cited by 15

PatchVAE: Learning Local Latent Codes for Recognition

Gupta, K., Singh, S., & Shrivastava, A. (2020) · 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/cvpr42600.2020.00480 · Cited by 17

Exploiting temporal coherence for self-supervised visual tracking by using vision transformer

Zhu, W., Wang, Z., Li, X., & Meng, J. H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109318 · Cited by 15

Contrastive Learning for Space-time Correspondence via Self-cycle Consistency

Son, J. (2022) · 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/cvpr52688.2022.01427 · Cited by 16

Knowledge-guided pre-training and fine-tuning: Video representation learning for action recognition

Wang, G., Zhou, Y., He, Z., Lu, K., Feng, Y., Liu, Z., & Wang, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127136 · Cited by 15

Cognitive Action Laws: The Case of Visual Features

Betti, A., Gori, M., & Melacci, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2911174 · Cited by 15

Image captioning with semantic-enhanced features and extremely hard negative examples

Cai, W., & Liu, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.112 · Cited by 15

Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex

Jia, X., Hong, H., & DiCarlo, J. J. (2021) · eLife · doi:10.7554/elife.60830 · Cited by 18

Anomaly detection of power line insulator from aerial imagery with attribute self-supervised learning

Ge, B., Hou, C., Liu, Y., Wang, Z., & Wu, R. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1934592 · Cited by 16

Exploiting Spatio-Temporal Structure With Recurrent Winner-Take-All Networks

Santana, E., Emigh, M. S., Zegers, P., & Príncipe, J. C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2735903 · Cited by 15

Self-Supervised Visual Representations for Cross-Modal Retrieval

Patel, Y., Gómez, L., Rusiñol, M., Karatzas, D., & Jawahar, C. V. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3325035 · Cited by 17

ActionFlowNet: Learning Motion Representation for Action RecognitionPreprint

Ng, J. Y.-H., Choi, J., Neumann, J., & Davis, L. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03052 · Cited by 15

SiT: Self-supervised vIsion Transformer

Atito, S., Awais, M., & Kittler, J. (2021) · arXiv · doi:10.48550/arxiv.2104.03602 · Cited by 75

Learning Contrastive Representation for Semantic Correspondence

Xiao, T., Liu, S., Mello, S. D., Yu, Z., Kautz, J., & Yang, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01602-y · Cited by 15

Federated Self-supervised Learning for Video Understanding

Rehman, Y. A. U., Gao, Y., Shen, J., de Gusmão, P. P. B., & Lane, N. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_29 · Cited by 14

Self-Supervised Learning for Text Recognition: A Critical Survey

Penarrubia, C., Valero-Mas, J. J., & Calvo-Zaragoza, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02487-3 · Cited by 19

DreamDiffusion: High-Quality EEG-to-Image Generation with Temporal Masked Signal Modeling and CLIP Alignment

Bai, Y., Wang, X., Cao, Y., Ge, Y., Yuan, C., & Shan, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_27 · Cited by 21

Echo-Vision-FM: a pre-training and fine-tuning framework for echocardiogram video vision foundation model

Zhang, Z., Wu, Q., Ding, S., Wang, X., & Ye, J. (2025) · Nature Communications · doi:10.1038/s41467-025-66340-4 · Cited by 19

ShapeCodes: Self-supervised Feature Learning by Lifting Views to Viewgrids

Jayaraman, D., Gao, R., & Grauman, K. L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_8 · Cited by 14

ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs

Mithun, N. C., Munir, S., Guo, K., & Shelton, C. (2018) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2018.00051 · Cited by 14

DeepShoe: An improved Multi-Task View-invariant CNN for street-to-shop shoe retrieval

Zhan, H., Shi, B., Duan, L., & Kot, A. C. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.01.001 · Cited by 14

Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations

Huo, X., Xie, L., Wei, L., Zhang, X., Chen, X., Li, H. L., Yang, Z., Zhou, W., Li, H., & Tian, Q. C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3115335 · Cited by 17

MaCLR: Motion-Aware Contrastive Learning of Representations for Videos

Xiao, F., Tighe, J., & Modolo, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_21 · Cited by 20

CC-SSL: A Self-Supervised Learning Framework for Crop Classification With Few Labeled Samples

Wang, H., Chang, W., Yao, Y., Liu, D., Zhao, Y., Li, S., Liu, Z., & Zhang, X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3211994 · Cited by 14

Formula-Driven Supervised Learning in Computer Vision: A Literature Survey

Hafiz, A. M., Hassaballah, M., & Binbusayyis, A. (2023) · Applied Sciences · doi:10.3390/app13020723 · Cited by 15

Semi-Supervised DFF

Han, G., Zhang, X., & Li, C. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240693 · Cited by 14

Extending Momentum Contrast With Cross Similarity Consistency Regularization

Seyfi, M., Banitalebi-Dehkordi, A., & Zhang, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3169145 · Cited by 14

Pose from Action: Unsupervised Learning of Pose Features based on MotionPreprint

Purushwalkam, S., & Gupta, A. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05420 · Cited by 14

Objects that SoundPreprint

Arandjelović, R., & Zisserman, A. (2017) · arXiv (Cornell University) · Cited by 14

Hardness-Aware Deep Metric LearningPreprint

Zheng, W., Chen, Z., Lu, J., & Zhou, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05503 · Cited by 14

Edge-Based Discovery of Training Data for Machine Learning

Feng, Z., George, S., Harkes, J., Pillai, P. S., Klatzky, R. L., & Satyanarayanan, M. (2018) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00018 · Cited by 13

Revisiting Hard Example for Action Recognition

Wang, J., Hu, J., Li, S., & Yuan, Z. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2978855 · Cited by 14

Consistent Intra-Video Contrastive Learning With Asynchronous Long-Term Memory Bank

Chen, Z., Lin, K.-Y., & Zheng, W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3207174 · Cited by 13

Progressive scene text erasing with self-supervision

Du, X., Zhou, Z., Zheng, Y., Wu, X., Ma, T., & Jin, C. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103712 · Cited by 13

Unconstrained Foreground Object Search

Zhao, Y., Price, B. L., Cohen, S. D., & Gurari, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00212 · Cited by 13

Learning user interest with improved triplet deep ranking and web-image priors for topic-related video summarization

Fei, M., Jiang, W., & Mao, W. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114036 · Cited by 13

Associative Compression Networks for Representation LearningPreprint

Graves, A., Menick, J., & van den Oord, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.02476 · Cited by 19

Self-Supervised Learning for Spinal MRIsPreprint

Jamaludin, A., Kadir, T., & Zisserman, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00367 · Cited by 15

Mutual Suppression Network for Video Prediction using Disentangled FeaturesPreprint

Lee, J., Lee, J., Lee, S., & Yoon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04810 · Cited by 16

Enhancing Person Re-Identification Performance Through In Vivo Learning

Huang, Y., Huang, Y., Zhang, Z., Wu, Q., Zhong, Y., & Wang, L. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3341762 · Cited by 13

Weakly supervised easy-to-hard learning for object detection in image sequences

Yu, H., Guo, D., Yan, Z., Fu, L., Simmons, J. P., Przybyla, C., & Wang, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.075 · Cited by 13

EspialCog: General, Efficient and Robust Mobile User Implicit Authentication in Noisy Environment

Zhu, T., Weng, Z., Song, Q., Yuan, C., Liu, Q., Yan, C., Lv, M., & Chen, T. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3012491 · Cited by 12

Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection

Wang, S., Wang, Z., Che, W., & Liu, T. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.142 · Cited by 13

Learning to Navigate Intersections with Unsupervised Driver Trait Inference

Liu, S., Chang, P., Chen, H., Chakraborty, N., & Driggs-Campbell, K. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811635 · Cited by 14

Thermal infrared object tracking via unsupervised deep correlation filters

Huang, Y., He, Y., Lü, R., Li, X., & Yang, X. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103432 · Cited by 12

An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

Gao, X., Mu, T., Goulermas, J. Y., Thiyagalingam, J., & Wang, M. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2965275 · Cited by 12

One-Shot Informed Robotic Visual Search in the Wild

Koreitem, K., Shkurti, F., Manderson, T., Chang, W.-D., Higuera, J. C. G., & Dudek, G. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340914 · Cited by 13

Distilling Visual Priors from Self-Supervised Learning

Zhao, B., & Wen, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_29 · Cited by 12

Spatio-Temporal Dual-Branch Network With Predictive Feature Learning for Satellite Video Object Segmentation

Zhong, Y., Shu, M., Liu, Z., & Lu, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3148310 · Cited by 12

Siamese Detail Difference and Self-Inverse Network for Forest Cover Change Extraction Based on Landsat 8 OLI Satellite Images

Guo, Y., Long, T., Jiao, W., Zhang, X. M., He, G., Wang, W., Peng, Y., & Xiao, H. (2022) · Remote Sensing · doi:10.3390/rs14030627 · Cited by 13

Multigrid Predictive Filter Flow for Unsupervised Learning on VideosPreprint

Kong, S., & Fowlkes, C. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01693 · Cited by 15

Self-Supervised Representation Learning for Visual Anomaly DetectionPreprint

Ali, R., Khan, M. U. K., & Kyung, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09654 · Cited by 19

Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation

Kuo, C.-W., Ma, C., Hoffman, J., & Kira, Z. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00116 · Cited by 11

ViC-MAE: Self-supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

Hernandez, J., Villegas, R., & Ordóñez, V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73235-5_25 · Cited by 15

Learning local descriptors by optimizing the keypoint-correspondence criterion

Markuš, N., Pandzic, I. S., & Ahlberg, J. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899992 · Cited by 10

Unsupervised incremental learning of deep descriptors from video streams

Pernici, F., & Bimbo, A. D. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026276 · Cited by 10

Data Selection in Neural Networks

Ferreira, J. O., Mendonça, M. O. K., & Diniz, P. S. R. (2021) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2021.3106197 · Cited by 11

Unsupervised Video-Based Action Recognition With Imagining Motion and Perceiving Appearance

Lin, W. Y., Liu, X., Zhuang, Y., Ding, X., Tu, X., Huang, Y., & Zeng, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3221280 · Cited by 11

FLAVR: flow-free architecture for fast video frame interpolation

Kalluri, T., Pathak, D., Chandraker, M. K., & Tran, D. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01433-y · Cited by 11

Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning

Gui, G., Zhao, Z. W., Qi, L., Zhou, L., Wang, L., & Shi, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01455 · Cited by 11

Sustained Self-Supervised Pretraining for Temporal Order Verification

Buckchash, H., & Raman, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34869-4_16 · Cited by 11

Self-Supervised Learning of Compressed Video Representations

Yu, Y., Lee, S., Kim, G., & Song, Y. (2021) · Seoul National University Open Repository (Seoul National University) · Cited by 12

Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation

Dong, G.-N., Pun, C.-M., & Zhang, Z. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3098165 · Cited by 11

Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image DatabasePreprint

Wang, X., Lü, L., Shin, H.-C., Kim, L., Nogues, I., Yao, J., & Summers, R. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07965 · Cited by 16

WebVision Challenge: Visual Learning and Understanding With Web DataPreprint

Li, W., Wang, L., Li, W., Agustsson, E., Berent, J., Gupta, A., Sukthankar, R., & Gool, L. V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.05640 · Cited by 11

Class Rectification Hard Mining for Imbalanced Deep LearningPreprint

Dong, Q., Gong, S., & Zhu, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.03162 · Cited by 15

Visuomotor Understanding for Representation Learning of Driving ScenesPreprint

Lee, S., Kim, J., Oh, T.-H., Jeong, Y., Yoo, D., Lin, S. C.-F., & Kweon, I. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06979 · Cited by 11

Probing Emergent Semantics in Predictive Agents via Question AnsweringPreprint

Das, A., Carnevale, F., Merzić, H., Rimell, L., Schneider, R. G., Abramson, J., Hung, A., Ahuja, A., Clark, S. C., Wayne, G., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01016 · Cited by 10

Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion ApproachPreprint

Swope, A., Rudelis, X., & Story, K. T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.05094 · Cited by 19

The Analysis of High Density Crowds in Videos

Rodriguez, M. D., Šivic, J., & Ivan Laptev, A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809276-7.00006-0 · Cited by 10

Predicting Facial Attributes in Video Using Temporal Coherence and Motion-Attention

Hand, E., Castillo, C. D., & Chellappa, R. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00017 · Cited by 10

NeuralCubes: Deep Representations for Visual Data Exploration

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