[19] Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, and Sanja Fidler. Editgan: High-precision semantic image editing. Advances in Neural Information Processing Systems, 34:16331–16345, 2021.
[20] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499, 2023.
[21] Wenhao Lu, Xiaochen Lian, and Alan Yuille. Parsing semantic parts of cars using graphical models and segment appearance consistency. arXiv preprint arXiv:1406.2375, 2014.
[22] Timo Lüddecke and Alexander Ecker. Image segmentation using text and image prompts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7086-7096, June 2022.
[23] Umberto Michieli and Pietro Zanuttigh. Edge-aware graph matching network for part-based semantic segmentation. International Journal of Computer Vision, 2022.
[24] Umberto Michieli, Edoardo Borsato, Luca Rossi, and Pietro Zanuttigh. Gmnet: Graph matching network for large scale part semantic segmentation in the wild. In Computer Vision-ECCV 2020: 16th European Conference, 2020.
[25] Tai-Yu Pan, Qing Liu, Wei-Lun Chao, and Brian Price. Towards open-world segmentation of parts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15392-15401, 2023.
[26] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[27] Mohit Shridhar, Lucas Manuelli, and Dieter Fox. CIoport: What and where pathways for robotic manipulation. In Conference on Robot Learning, pages 894-906. PMLR, 2022.
[28] Peize Sun, Shoufa Chen, Chenchen Zhu, Fanyi Xiao, Ping Luo, Saining Xie, and Zhicheng Yan. Going denser with open-vocabulary part segmentation. arXiv preprint arXiv:2305.11173, 2023.
[29] Chufeng Tang, Lingxi Xie, Xiaopeng Zhang, Xiaolin Hu, and Qi Tian. Visual recognition by request. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[30] Jianyu Wang and Alan L Yuille. Semantic part segmentation using compositional model combining shape and appearance. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.
[31] Chenyun Wu, Zhe Lin, Scott Cohen, Trung Bui, and Subhransu Maji. Phrasecut: Language-based image segmentation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10216-10225, 2020.
[32] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models. arXiv preprint arXiv:2303.04803, 2023.
[33] Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Han Hu, and Xiang Bai. A simple baseline for zero-shot semantic segmentation with pre-trained vision-language model. arXiv preprint arXiv:2112.14757, 2021.
[34] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[35] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127:302-321, 2019.
[36] Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. In European Conference on Computer Vision (ECCV), 2022.
[37] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16816-16825, 2022.