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Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., Mikolov, T.: Devise: A deep visual-semantic embedding model. In: NeurIPS. vol. 26 (2013)
2013
Earlier work this paper cites.
Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: Zero-shot learning through cross-modal transfer. NeurIPS 26
2013
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: ECCV. pp. 740–755. Springer International Publishing, Cham (2014)
2014
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: NeurIPS. vol. 28. Curran Associates, Inc. (2015)
2015
Earlier work this paper cites.
Biswas, S.K., Milanfar, P.: One shot detection with laplacian object and fast matrix cosine similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 38
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (June 2016)
2016
Earlier work this paper cites.
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: ECCV. pp. 646–661. Springer International Publishing, Cham (2016)
2016
Earlier work this paper cites.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: ECCV. pp. 21–37. Springer International Publishing, Cham (2016)
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
2017
Earlier work this paper cites.
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. International journal of computer vision 123
2017
Earlier work this paper cites.
Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-shot object detection. In: ECCV (September 2018)
2018
Earlier work this paper cites.
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M.J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., Zhang, Q.: JAX: composable transformations of Python+NumPy programs (2018), http://github.com/google/jax
2018
Earlier work this paper cites.
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., van der Maaten, L.: Exploring the limits of weakly supervised pretraining. In: ECCV. pp. 185–201. Springer International Publishing, Cham (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE transactions on pattern analysis and machine intelligence 41
2018
Earlier work this paper cites.
Gupta, A., Dollar, P., Girshick, R.: LVIS: A dataset for large vocabulary instance segmentation. In: CVPR (June 2019)
2019
Earlier work this paper cites.
Hsieh, T.I., Lo, Y.C., Chen, H.T., Liu, T.L.: One-shot object detection with co-attention and co-excitation. In: NeurIPS. vol. 32. Curran Associates, Inc. (2019)
2019
Cited alongside, same era.
Lee, J., Lee, Y., Kim, J., Kosiorek, A.R., Choi, S., Teh, Y.W.: Set transformer: A framework for attention-based permutation-invariant neural networks. In: ICML. Proceedings of Machine Learning Research, vol. 97, pp. 3744–3753. PMLR (2019)
2019
Cited alongside, same era.
Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., Sun, J.: Objects365: A Large-Scale, High-Quality Dataset for Object Detection. In: ICCV. pp. 8429–8438 (2019)
2019
Cited alongside, same era.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV. pp. 213–229. Springer International Publishing, Cham (2020)
2020
Cited alongside, same era.
Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: MDETR - modulated detection for end-to-end multi-modal understanding. In: ICCV. pp. 1780–1790 (2021)
2021
Later among the works it cites.
Kolesnikov, A., Dosovitskiy, A., Weissenborn, D., Heigold, G., Uszkoreit, J., Beyer, L., Minderer, M., Dehghani, M., Houlsby, N., Gelly, S., Unterthiner, T., Zhai, X.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
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2020
Cited alongside, same era.
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., Houlsby, N.: Big transfer (BiT): General visual representation learning. In: ECCV. pp. 491–507. Springer International Publishing, Cham (2020)
2020
Cited alongside, same era.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., Ferrari, V.: The Open Images Dataset V4. International Journal of Computer Vision 128
2020
Cited alongside, same era.
Osokin, A., Sumin, D., Lomakin, V.: OS2D: One-stage one-shot object detection by matching anchor features. In: ECCV. pp. 635–652. Springer International Publishing, Cham (2020)
2020
Cited alongside, same era.
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: ViViT: A video vision transformer. In: ICCV. pp. 6836–6846 (October 2021)
2021
Cited alongside, same era.
Bello, I., Fedus, W., Du, X., Cubuk, E.D., Srinivas, A., Lin, T.Y., Shlens, J., Zoph, B.: Revisiting ResNets: Improved training and scaling strategies. NeurIPS 34
2021
Cited alongside, same era.
Chen, D.J., Hsieh, H.Y., Liu, T.L.: Adaptive image transformer for one-shot object detection. In: CVPR. pp. 12242–12251 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: ICML. vol. 139, pp. 8748–8763. PMLR (18–24 Jul 2021)
2021
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2021
Later among the works it cites.
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jegou, H.: Training data-efficient image transformers and distillation through attention. In: ICML. vol. 139, pp. 10347–10357 (July 2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: CVPR. pp. 14393–14402 (June 2021)
2021
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2021
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2021
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2021
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2021
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2021
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Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: Deformable transformers for end-to-end object detection. In: ICLR (2021)
2021
Later among the works it cites.
Song, H., Sun, D., Chun, S., Jampani, V., Han, D., Heo, B., Kim, W., Yang, M.H.: ViDT: An efficient and effective fully transformer-based object detector. In: ICLR (2022)
2022
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