Fetching the paper…
Reading the bibliography…
Pretraining general-purpose visual features has become a crucial part of tackling many computer vision tasks.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR
2003
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
2007
Earlier work this paper cites.
Quattoni, A., Collins, M., Darrell, T.: Learning visual representations using images with captions. In: Proc. CVPR (2007)
2007
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: Proc. CVPR (2009)
2009
Earlier work this paper cites.
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. Tech. Rep. CNS-TR-2011-001, California Institute of Technology (2011)
2011
Earlier work this paper cites.
Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: Proc. CVPR (2012)
2012
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Proc. ECCV (2014)
2014
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. JMLR
2014
Earlier work this paper cites.
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Proc. NeurIPS (2014)
2014
Earlier work this paper cites.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proc. ICLR (2015)
2015
Earlier work this paper cites.
Chen, X., Gupta, A.: Webly supervised learning of convolutional networks. In: Proc. ICCV (2015)
2015
Earlier work this paper cites.
Deshpande, A., Rock, J., Forsyth, D.: Learning large-scale automatic image colorization. In: Proc. ICCV (2015)
2015
Earlier work this paper cites.
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proc. ICCV (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Proc. NeurIPS (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proc. ICLR (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv:1607.06450 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc. CVPR (2016)
2016
Earlier work this paper cites.
Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., Darrell, T.: Natural language object retrieval. In: Proc. CVPR (2016)
2016
Earlier work this paper cites.
Joulin, A., van der Maaten, L., Jabri, A., Vasilache, N.: Learning visual features from large weakly supervised data. In: Proc. ECCV (2016)
2016
Earlier work this paper cites.
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: Proc. CVPR (2016)
2016
Earlier work this paper cites.
Wang, L., Li, Y., Lazebnik, S.: Learning deep structure-preserving image-text embeddings. In: Proc. CVPR (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Proc. ECCV (2016)
2016
Earlier work this paper cites.
Doersch, C., Zisserman, A.: Multi-task self-supervised visual learning. In: Proc. ICCV (2017)
2017
Cited alongside, same era.
Fernando, B., Bilen, H., Gavves, E., Gould, S.: Self-supervised video representation learning with odd-one-out networks. In: Proc. CVPR (2017)
2017
Cited alongside, same era.
Gomez, L., Patel, Y., nol, M.R., Karatzas, D., Jawahar, C.: Self-supervised learning of visual features through embedding images into text topic spaces. In: Proc. CVPR (2017)
2017
Cited alongside, same era.
Gordo, A., Almazan, J., Revaud, J., Larlus, D.: End-to-end learning of deep visual representations for image retrieval. IJCV (2017)
2017
Cited alongside, same era.
Gordo, A., Larlus, D.: Beyond instance-level image retrieval: Leveraging captions to learn a global visual representation for semantic retrieval. In: Proc. CVPR (2017)
2017
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proc. CVPR (2018)
2018
Later among the works it cites.
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. PAMI
2018
Later among the works it cites.
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proc. ICKDDM (2019)
2019
Later among the works it cites.
Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. In: Proc. NeurIPS (2019)
2019
Later among the works it cites.
Caron, M., Bojanowski, P., Mairal, J., Joulin, A.: Unsupervised Pre-Training of Image Features on Non-Curated Data. In: Proc. ICCV (2019)
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering. In: Proc. CVPR (2017)
2017
Cited alongside, same era.
Hong, S., Yeo, D., Kwak, S., Lee, H., Han, B.: Weakly supervised semantic segmentation using web-crawled videos. In: Proc. CVPR (2017)
2017
Cited alongside, same era.
Honnibal, M., Montani, I.: spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. To appear (2017),
2017
Cited alongside, same era.
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV (2017)
2017
Cited alongside, same era.
Larsson, G., Maire, M., Shakhnarovich, G.: Colorization as a proxy task for visual understanding. In: Proc. CVPR (2017)
2017
Cited alongside, same era.
Li, A., Jabri, A., Joulin, A., van der Maaten, L.: Learning visual n-grams from web data. In: Proc. ICCV (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proc. NeurIPS (2017)
2017
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: ACL (2019)
2019
Later among the works it cites.
Gomez, R., Gomez, L., Gibert, J., Karatzas, D.: Chapter 9 - self-supervised learning from web data for multimodal retrieval. In: Multimodal Scene Understanding (2019)
2019
Later among the works it cites.
Goyal, P., Mahajan, D., Gupta, A., Misra, I.: Scaling and benchmarking self-supervised visual representation learning. In: Proc. ICCV (2019)
2019
Later among the works it cites.
Kolesnikov, A., Zhai, X., Beyer, L.: Revisiting Self-Supervised Visual Representation Learning. In: Proc. CVPR (2019)
2019
Later among the works it cites.
Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: Proc. CVPR (2019)
2019
Later among the works it cites.
Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Proc. NeurIPS (2019)
2019
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Proc. NeurIPS (2019)
2019
Later among the works it cites.
Sariyildiz, M.B., Cinbis, R.G.: Gradient matching generative networks for zero-shot learning. In: Proc. CVPR (2019)
2019
Later among the works it cites.
Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: Videobert: A joint model for video and language representation learning. In: Proc. ICCV (2019)
2019
Later among the works it cites.
Tan, H., Bansal, M.: Lxmert: Learning cross-modality encoder representations from transformers. In: Proc. EMNLP (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proc. ICML (2020)
2020
Closest in time.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proc. CVPR (2020)
2020
Closest in time.
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J.: On the variance of the adaptive learning rate and beyond. In: Proc. ICLR (2020)
2020
Closest in time.
2020
Closest in time.
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., Dai, J.: Vl-bert: Pre-training of generic visual-linguistic representations. In: Proc. ICLR (2020)
2020
Closest in time.
Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. Proc. CVPR (2020)
2020
Closest in time.