Fetching the paper…
Reading the bibliography…
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs).
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.
Jayaraman, D., Grauman, K.: Zero-shot recognition with unreliable attributes. In: NeurIPS (2014)
2014
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 (2014)
2014
Earlier work this paper cites.
Milletari, F., Navab, N., Ahmadi, S.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3DV (2016)
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
Gupta, A., Dollar, P., Girshick, R.: LVIS: A dataset for large vocabulary instance segmentation. In: CVPR (2019)
2019
Earlier work this paper cites.
Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: Panoptic segmentation. In: CVPR (2019)
2019
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)
2019
Earlier work this paper cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. In: NeurIPS (2019)
2019
Earlier work this paper cites.
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 (2019)
2019
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.
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. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tong, Y.: Semantic flow for fast and accurate scene parsing. In: ECCV (2020)
2020
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.
Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: ICLR (2021)
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Kim, W., Son, B., Kim, I.: ViLT: Vision-and-language transformer without convolution or region supervision. In: ICML (2021)
2021
Earlier work this paper cites.
Li, J., Selvaraju, R.R., Gotmare, A., Joty, S.R., Xiong, C., Hoi, S.C.: Align before fuse: Vision and language representation learning with momentum distillation. In: NeurIPS (2021)
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
2021
Earlier work this paper cites.
Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: CVPR (2021)
2021
Earlier work this paper cites.
Bao, H., Dong, L., Piao, S., Wei, F.: BEiT: BERT pre-training of image transformers. In: ICLR (2022)
2022
Earlier work this paper cites.
Bar, A., Gandelsman, Y., Darrell, T., Globerson, A., Efros, A.: Visual prompting via image inpainting. In: NeurIPS (2022)
2022
Earlier work this paper cites.
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR (2022)
2022
Earlier work this paper cites.
Ding, J., Xue, N., Xia, G.S., Dai, D.: Decoupling zero-shot semantic segmentation. In: CVPR (2022)
2022
Earlier work this paper cites.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR (2022)
2022
Earlier work this paper cites.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: ICLR (2022)
2022
Earlier work this paper cites.
Huynh, D., Kuen, J., Lin, Z., Gu, J., Elhamifar, E.: Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In: CVPR (2022)
2022
Earlier work this paper cites.
Kim, D., Lin, T.Y., Angelova, A., Kweon, I.S., Kuo, W.: Learning open-world object proposals without learning to classify. RA-L (2022)
2022
Earlier work this paper cites.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: ICML (2022)
2022
Earlier work this paper cites.
Lian, D., Zhou, D., Feng, J., Wang, X.: Scaling & shifting your features: A new baseline for efficient model tuning. In: NeurIPS (2022)
2022
Cited alongside, same era.
Maaz, M., Rasheed, H., Khan, S., Khan, F.S., Anwer, R.M., Yang, M.H.: Class-agnostic object detection with multi-modal transformer. In: ECCV (2022)
2022
Cited alongside, same era.
Qi, L., Kuen, J., Wang, Y., Gu, J., Zhao, H., Torr, P., Lin, Z., Jia, J.: Open world entity segmentation. IEEE TPAMI (2022)
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)
2022
Cited alongside, same era.
Xu, M., Zhang, Z., Wei, F., Lin, Y., Cao, Y., Hu, H., Bai, X.: A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model. In: ECCV (2022)
2022
Qi, L., Kuen, J., Shen, T., Gu, J., Guo, W., Jia, J., Lin, Z., Yang, M.H.: High-quality entity segmentation. In: ICCV (2023)
2023
Later among the works it cites.
Shi, C., Yang, S.: Edadet: Open-vocabulary object detection using early dense alignment. In: ICCV (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, J., Zhang, P., Chu, T., Cao, Y., Zhou, Y., Wu, T., Wang, B., He, C., Lin, D.: V3det: Vast vocabulary visual detection dataset. In: ICCV (2023)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., Lu, J.: Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In: CVPR (2022)
2022
Cited alongside, same era.
Zang, Y., Li, W., Zhou, K., Huang, C., Loy, C.C.: Open-vocabulary DETR with conditional matching. In: ECCV (2022)
2022
Cited alongside, same era.
Zhai, X., Wang, X., Mustafa, B., Steiner, A., Keysers, D., Kolesnikov, A., Beyer, L.: LiT: Zero-shot transfer with locked-image text tuning. In: CVPR (2022)
2022
Cited alongside, same era.
Zhou, C., Loy, C.C., Dai, B.: Extract free dense labels from CLIP. In: ECCV (2022)
2022
Cited alongside, same era.
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. IJCV (2022)
2022
Cited alongside, same era.
Zhou, X., Girdhar, R., Joulin, A., Krähenbühl, P., Misra, I.: Detecting twenty-thousand classes using image-level supervision. In: ECCV (2022)
2022
Cited alongside, same era.
Balažević, I., Steiner, D., Parthasarathy, N., Arandjelović, R., Hénaff, O.J.: Towards in-context scene understanding. In: NeurIPS (2023)
2023
Cited alongside, same era.
2023
Later among the works it cites.
Wang, X., Wang, W., Cao, Y., Shen, C., Huang, T.: Images speak in images: A generalist painter for in-context visual learning. In: CVPR (2023)
2023
Later among the works it cites.
Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., Huang, T.: Seggpt: Segmenting everything in context. In: ICCV (2023)
2023
Later among the works it cites.
Wu, J., Li, X., Ding, H., Li, X., Cheng, G., Tong, Y., Loy, C.C.: Betrayed by captions: Joint caption grounding and generation for open vocabulary instance segmentation. In: ICCV (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, S., Zhang, W., Jin, S., Liu, W., Loy, C.C.: Aligning bag of regions for open-vocabulary object detection. In: CVPR (2023)
2023
Later among the works it cites.
Wu, X., Zhu, F., Zhao, R., Li, H.: CORA: Adapting CLIP for open-vocabulary detection with region prompting and anchor pre-matching. In: CVPR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
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
Later among the works it cites.
Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X.: Side adapter network for open-vocabulary semantic segmentation. In: CVPR (2023)
2023
Later among the works it cites.
Xu, S., Li, X., Wu, S., Zhang, W., Li, Y., Cheng, G., Tong, Y., Chen, K., Loy, C.C.: Dst-det: Simple dynamic self-training for open-vocabulary object detection. arXiv pre-print (2023)
2023
Later among the works it cites.
Xu, X., Xiong, T., Ding, Z., Tu, Z.: Masqclip for open-vocabulary universal image segmentation. In: ICCV (2023)
2023
Later among the works it cites.
Yang, J., Peng, W., Li, X., Guo, Z., Chen, L., Li, B., Ma, Z., Zhou, K., Zhang, W., Loy, C.C., et al.: Panoptic video scene graph generation. In: CVPR (2023)
2023
Later among the works it cites.
Yu, Q., He, J., Deng, X., Shen, X., Chen, L.C.: Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional CLIP. In: NeurIPS (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Zou, X., Yang, J., Zhang, H., Li, F., Li, L., Gao, J., Lee, Y.J.: Segment everything everywhere all at once. In: NeurIPS (2023)
2023
Later among the works it cites.
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., Qiao, Y.: CLIP-Adapter: Better vision-language models with feature adapters. IJCV (2024)
2024
Closest in time.
2024
Closest in time.
Li, X., Yuan, H., Li, W., Ding, H., Wu, S., Zhang, W., Li, Y., Chen, K., Loy, C.C.: OMG-Seg: Is one model good enough for all segmentation? In: CVPR (2024)
2024
Closest in time.
Wu, J., Li, X., Xu, S., Yuan, H., Ding, H., Yang, Y., Li, X., Zhang, J., Tong, Y., Jiang, X., Ghanem, B., Tao, D.: Towards open vocabulary learning: A survey. IEEE TPAMI (2024)
2024
Closest in time.
Wu, S., Zhang, W., Xu, L., Jin, S., Li, X., Liu, W., Loy, C.C.: CLIPSelf: Vision transformer distills itself for open-vocabulary dense prediction. In: ICLR (2024)
2024
Closest in time.