Fetching the paper…
Reading the bibliography…
Recent advances in contrastive language-image pretraining (CLIP) have demonstrated strong capabilities in zero-shot classification by aligning visual representations with target text embeddings in an image level.
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.
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. NeurIPS (2011)
2011
Earlier work this paper cites.
Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., Yuille, A.: The role of context for object detection and semantic segmentation in the wild. In: CVPR (2014)
2014
Earlier work this paper cites.
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. IJCV (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML (2015)
2015
Earlier work this paper cites.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: Thing and stuff classes in context. In: CVPR (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., Torralba, A.: Semantic understanding of scenes through the ade20k dataset. IJCV (2019)
2019
Earlier work this paper cites.
Araslanov, N., Roth, S.: Single-stage semantic segmentation from image labels. In: CVPR (2020)
2020
Earlier work this paper cites.
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. NeurIPS (2020)
2020
Earlier work this paper cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV (2020)
2020
Earlier work this paper cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)
2020
Earlier work this paper cites.
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., Valko, M.: Bootstrap your own latent a new approach to self-supervised learning. In: NeurIPS (2020)
2020
Earlier work this paper cites.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research (2020)
2020
Earlier work this paper cites.
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.C.: Axial-deeplab: Stand-alone axial-attention for panoptic segmentation. In: ECCV (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)
2021
Earlier work this paper cites.
Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. NeurIPS (2021)
2021
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. NeurIPS (2021)
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Hénaff, O.J., Koppula, S., Alayrac, J.B., Van den Oord, A., Vinyals, O., Carreira, J.: Efficient visual pretraining with contrastive detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10086–10096 (2021)
2021
Cited alongside, same era.
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: ICML (2021)
2021
Cited alongside, same era.
Wei, C., Fan, H., Xie, S., Wu, C.Y., Yuille, A., Feichtenhofer, C.: Masked feature prediction for self-supervised visual pre-training. In: CVPR (2022)
2022
Later among the works it cites.
Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G.: Vision transformer with deformable attention. In: CVPR (2022)
2022
Later among the works it cites.
Xu, J., De Mello, S., Liu, S., Byeon, W., Breuel, T., Kautz, J., Wang, X.: Groupvit: Semantic segmentation emerges from text supervision. In: CVPR (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Zhou, C., Loy, C.C., Dai, B.: Extract free dense labels from clip. In: ECCV (2022)
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV (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., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: ICCV (2021)
2021
Cited alongside, same era.
Wang, H., Zhu, Y., Adam, H., Yuille, A., Chen, L.C.: Max-deeplab: End-to-end panoptic segmentation with mask transformers. In: CVPR (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Later among the works it cites.
Cha, J., Mun, J., Roh, B.: Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs. In: CVPR (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Li, K., Wang, Z., Cheng, Z., Yu, R., Zhao, Y., Song, G., Liu, C., Yuan, L., Chen, J.: Acseg: Adaptive conceptualization for unsupervised semantic segmentation. In: CVPR (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Luo, H., Bao, J., Wu, Y., He, X., Li, T.: Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation. In: ICML (2023)
2023
Closest in time.
Mukhoti, J., Lin, T.Y., Poursaeed, O., Wang, R., Shah, A., Torr, P.H., Lim, S.N.: Open vocabulary semantic segmentation with patch aligned contrastive learning. In: CVPR (2023)
2023
Closest in time.
Ranasinghe, K., McKinzie, B., Ravi, S., Yang, Y., Toshev, A., Shlens, J.: Perceptual grouping in contrastive vision-language models. In: ICCV (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: CVPR (2023)
2023
Closest in time.
Xu, J., Hou, J., Zhang, Y., Feng, R., Wang, Y., Qiao, Y., Xie, W.: Learning open-vocabulary semantic segmentation models from natural language supervision. In: CVPR (2023)
2023
Closest in time.
Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X.: Side adapter network for open-vocabulary semantic segmentation. In: CVPR (2023)
2023
Closest in time.
Yi, M., Cui, Q., Wu, H., Yang, C., Yoshie, O., Lu, H.: A simple framework for text-supervised semantic segmentation. In: CVPR (2023)
2023
Closest in time.
2023
Closest in time.
Yun, S., Park, S.H., Seo, P.H., Shin, J.: Ifseg: Image-free semantic segmentation via vision-language model. In: CVPR (2023)
2023
Closest in time.
Zhou, Z., Lei, Y., Zhang, B., Liu, L., Liu, Y.: Zegclip: Towards adapting clip for zero-shot semantic segmentation. In: CVPR (2023)
2023
Closest in time.
Zhu, L., Wang, X., Ke, Z., Zhang, W., Lau, R.W.: Biformer: Vision transformer with bi-level routing attention. In: CVPR (2023)
2023
Closest in time.