{"type":"ScholarlyArticle","title":"P2P: Transforming from Point Supervision to Explicit Visual Prompt for Object Detection and Segmentation","authors":[{"name":"Guangqian Guo","slug":"guangqian-guo","given":"Guangqian","family":"Guo","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"},{"name":"Dian Shao","slug":"dian-shao","given":"Dian","family":"Shao","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"},{"name":"Chenguang Zhu","slug":"chenguang-zhu","given":"Chenguang","family":"Zhu","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"},{"name":"Sha Meng","slug":"sha-meng","given":"Sha","family":"Meng","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"},{"name":"Xuan Wang","slug":"xuan-wang","given":"Xuan","family":"Wang","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"},{"name":"Shan Gao","slug":"shan-gao","given":"Shan","family":"Gao","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"}],"abstract":"Point-supervised vision tasks, including detection and segmentation, aiming to learn a network that transforms from points to pseudo labels, have attracted much attention in recent years. However, the lack of precise object size and boundary annotations in the point-supervised condition results in a large performance gap between point- and fully-supervised methods. In this paper, we propose a novel iterative learning framework, Point to Prompt (P2P), for point-supervised object detection and segmentation, with the key insight of transforming from point supervision to explicit visual prompt of the foundation model. The P2P is formulated as an iterative refinement process of two stages: Semantic Explicit Prompt Generation (SEPG) and Prompt Guided Spatial Refinement (PGSR). Specifically, SEPG serves as a prompt generator for generating semantic-explicit prompts from point input via a group-based learning strategy. In the PGSR stage, prompts guide the visual foundation model to further refine the object regions, by leveraging the outstanding generalization ability of the foundation model. The two stages are iterated multiple times to improve the quality of predictions progressively. Experimental results on multiple datasets demonstrate that P2P achieves SOTA performance in both detection and segmentation tasks, further narrowing the performance gap with fully-supervised methods. The source code and supplementary material can be found at https://github.com/guangqian-guo/P2P.","doi":"10.24963/ijcai.2024/448","url":"https://ijcai.org/proceedings/2024/448","pdf":"https://ijcai.org/proceedings/2024/0448.pdf","markdown":"https://ijcai.org/proceedings/2024/448.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/448","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":448,"pages":{"first":4053,"last":4061},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Recognition (object detection, categorization)","Machine Learning: ML: Weakly supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Arbeláez et al., 2014] Pablo Arbeláez, Jordi Pont-Tuset, Jonathan T Barron, Ferran Marques, and Jitendra Malik. Multiscale combinatorial grouping. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 328-335, 2014.","title":"Multiscale combinatorial grouping","authors":"Arbeláez","year":"2014","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bilen and Vedaldi, 2016] Hakan Bilen and Andrea Vedaldi. Weakly supervised deep detection networks. In Proceed- ings of the IEEE conference on computer vision and pat- tern recognition, pages 2846-2854, 2016.","title":"Weakly supervised deep detection networks","authors":"Hakan Bilen, Andrea Vedaldi","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2022] Pengfei Chen, Xuehui Yu, Xumeng Han, Najmul Hassan, Kai Wang, Jiachen Li, Jian Zhao, Humphrey Shi, Zhenjun Han, and Qixiang Ye. Point-to- box network for accurate object detection via single point supervision. In European Conference on Computer Vision, pages 51-67. Springer, 2022.","title":"Point-tobox network for accurate object detection via single point supervision","authors":"Pengfei Chen, Xuehui Yu, Xumeng Han, Najmul Hassan, Kai Wang, Jiachen Li, Jian Zhao, Humphrey Shi, Zhenjun Han, Qixiang Ye","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2023a] Keyan Chen, Chenyang Liu, Hao Chen, Haotian Zhang, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi. Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model. arXiv preprint arXiv:2306.16269, 2023.","title":"Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model","authors":"Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2023b] Tianle Chen, Zheda Mai, Ruiwen Li, and Wei-lun Chao. Segment anything model (sam) en- hanced pseudo labels for weakly supervised semantic seg- mentation. arXiv preprint arXiv:2305.05803, 2023.","title":"Segment anything model (sam) enhanced pseudo labels for weakly supervised semantic segmentation","authors":"Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2022] Bowen Cheng, Ishan Misra, Alexan- der G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1290- 1299, 2022.","title":"Masked-attention mask transformer for universal image segmentation","authors":"Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, Rohit Girdhar","year":"2022","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Everingham et al., 2015] Mark Everingham, SM Ali Es- lami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes challenge: A retrospective. International journal of com- puter vision, 111:98-136, 2015.","title":"The pascal visual object classes challenge: A retrospective","authors":"Mark Everingham, Ali Sm, Luc Eslami, Van Gool, K I Christopher, John Williams, Andrew Winn, Zisserman","year":"2015","venue":"International journal of computer vision","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gao et al., 2019] Yan Gao, Boxiao Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, and Dongrui Fan. C-midn: Coupled multiple instance detection network with segmen- tation guidance for weakly supervised object detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 9834-9843, 2019.","title":"C-midn: Coupled multiple instance detection network with segmentation guidance for weakly supervised object detection","authors":"Yan Gao, Boxiao Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, Dongrui Fan","year":"2019","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2017] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017.","title":"Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn","authors":"He","year":"2017","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He et al., 2023] Sheng He, Rina Bao, Jingpeng Li, P Ellen Grant, and Yangming Ou. Accuracy of segment-anything model (sam) in medical image segmentation tasks. arXiv preprint arXiv:2304.09324, 2023.","title":"Accuracy of segment-anything model (sam) in medical image segmentation tasks","authors":"Sheng He, Rina Bao, Jingpeng Li, Ellen Grant, Yangming Ou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2020] Zeyi Huang, Yang Zou, BVK Ku- mar, and Dong Huang. Comprehensive attention self-distillation for weakly-supervised object detection. Advances in neural information processing systems, 33:16797-16807, 2020.","title":"Comprehensive attention self-distillation for weakly-supervised object detection","authors":"Zeyi Huang, Yang Zou, Dong Kumar, Huang","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kim et al., 2022] Beomyoung Kim, Youngjoon Yoo, Chae Eun Rhee, and Junmo Kim. Beyond semantic to instance segmentation: Weakly-supervised instance segmentation via semantic knowledge transfer and self- refinement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4278-4287, 2022.","title":"Beyond semantic to instance segmentation: Weakly-supervised instance segmentation via semantic knowledge transfer and selfrefinement","authors":"Kim","year":"2022","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kirillov et al., 2023] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643, 2023.","title":"Segment anything","authors":"Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lan et al., 2021] Shiyi Lan, Zhiding Yu, Christopher Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry S Davis, and Anima Anandkumar. Discobox: Weakly super- vised instance segmentation and semantic correspondence from box supervision. In Proceedings of the IEEE Interna- tional Conference on Computer Vision, pages 3406-3416, 2021.","title":"Discobox: Weakly supervised instance segmentation and semantic correspondence from box supervision","authors":"Shiyi Lan, Zhiding Yu, Christopher Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry S Davis, Anima Anandkumar","year":"2021","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Laradji et al., 2020] Issam H Laradji, Negar Rostamzadeh, Pedro O Pinheiro, David Vazquez, and Mark Schmidt. Proposal-based instance segmentation with point supervi- sion. In 2020 IEEE International Conference on Image Processing, pages 2126-2130. IEEE, 2020.","title":"Proposal-based instance segmentation with point supervision","authors":"Negar Issam H Laradji, Pedro O Rostamzadeh, David Pinheiro, Mark Vazquez, Schmidt","year":"2020","venue":"IEEE International Conference on Image Processing","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liao et al., 2022] Mingxiang Liao, Fang Wan, Yuan Yao, Zhenjun Han, Jialing Zou, Yuze Wang, Bailan Feng, Peng Yuan, and Qixiang Ye. End-to-end weakly supervised ob- ject detection with sparse proposal evolution. In European Conference on Computer Vision, pages 210-226. Springer, 2022.","title":"End-to-end weakly supervised object detection with sparse proposal evolution","authors":"Mingxiang Liao, Fang Wan, Yuan Yao, Zhenjun Han, Jialing Zou, Yuze Wang, Bailan Feng, Peng Yuan, Qixiang Ye","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liao et al., 2023] Mingxiang Liao, Zonghao Guo, Yuze Wang, Peng Yuan, Bailan Feng, and Fang Wan. Attention- shift: Iteratively estimated part-based attention map for pointly supervised instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 19519-19528, 2023.","title":"Attentionshift: Iteratively estimated part-based attention map for pointly supervised instance segmentation","authors":"Liao","year":"2023","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lin et al., 2014] Tsung-Yi Lin, Michael Maire, Serge Be- longie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Com- mon objects in context. In Proceedings of the European Conference on Computer Vision, pages 740-755. Springer, 2014.","title":"Microsoft coco: Common objects in context","authors":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C Lawrence, Zitnick","year":"2014","venue":"Proceedings of the European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lin et al., 2017] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense ob- ject detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 2980-2988, 2017.","title":"Focal loss for dense object detection","authors":"Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár","year":"2017","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2021] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE international conference on computer vision, pages 10012-10022, 2021.","title":"Swin transformer: Hierarchical vision transformer using shifted windows","authors":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo","year":"2021","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Qi, 2023] Lai Qi. Towards precise weakly supervised object detection via interactive contrastive learning of context in- formation. arXiv preprint arXiv:2304.14114, 2023.","title":"Towards precise weakly supervised object detection via interactive contrastive learning of context information","authors":"Lai Qi","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ren et al., 2015] Shaoqing Ren, Kaiming He, Ross Gir- shick, and Jian Sun. Faster r-cnn: Towards real-time ob- ject detection with region proposal networks. Advances in neural information processing systems, 28, 2015.","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","authors":"Ren","year":"2015","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ren et al., 2020a] Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Yong Jae Lee, Alexander G Schwing, and Jan Kautz. Instance-aware, context-focused, and memory-efficient weakly supervised object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 10598-10607, 2020.","title":"Instance-aware, context-focused, and memory-efficient weakly supervised object detection","authors":"Ren","year":"2020","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ren et al., 2020b] Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Alexander G Schwing, and Jan Kautz. Ufo 2: A unified framework towards omni-supervised ob- ject detection. In European conference on computer vi- sion, pages 288-313. Springer, 2020.","title":"Ufo 2: A unified framework towards omni-supervised object detection","authors":"Ren","year":"2020","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Robbins and Monro, 1951] Herbert Robbins and Sutton Monro. A stochastic approximation method. The annals of mathematical statistics, pages 400-407, 1951.","title":"The annals of mathematical statistics","authors":"Monro Robbins, Herbert Robbins, Sutton Monro","year":"1951","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Seo et al., 2022] Jinhwan Seo, Wonho Bae, Danica J Sutherland, Junhyug Noh, and Daijin Kim. Object discov- ery via contrastive learning for weakly supervised object detection. In European Conference on Computer Vision, pages 312-329. Springer, 2022.","title":"Object discovery via contrastive learning for weakly supervised object detection","authors":"Seo","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sun et al., 2021] Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, et al. Sparse r-cnn: End- to-end object detection with learnable proposals. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 14454-14463, 2021.","title":"Sparse r-cnn: Endto-end object detection with learnable proposals","authors":"Sun","year":"2021","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tang et al., 2017] Peng Tang, Xinggang Wang, Xiang Bai, and Wenyu Liu. Multiple instance detection network with online instance classifier refinement. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 2843-2851, 2017.","title":"Multiple instance detection network with online instance classifier refinement","authors":"Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Tang et al., 2018] Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, and Alan Yuille. Pcl: Proposal cluster learning for weakly supervised object de- tection. IEEE transactions on pattern analysis and ma- chine intelligence, 42(1):176-191, 2018.","title":"Pcl: Proposal cluster learning for weakly supervised object detection","authors":"Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille","year":"2018","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tian et al., 2021] Zhi Tian, Chunhua Shen, Xinlong Wang, and Hao Chen. Boxinst: High-performance instance seg- mentation with box annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pages 5443-5452, 2021.","title":"Boxinst: High-performance instance segmentation with box annotations","authors":"Chunhua Zhi Tian, Xinlong Shen, Hao Wang, Chen","year":"2021","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Touvron et al., 2021] Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou. Going deeper with image transformers. In Proceedings of the IEEE international conference on computer vision, pages 32-42, 2021.","title":"Going deeper with image transformers","authors":"Touvron","year":"2021","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Uijlings et al., 2013] Jasper RR Uijlings, Koen EA Van De Sande, Theo Gevers, and Arnold WM Smeulders. Se- lective search for object recognition. International journal of computer vision, 104:154-171, 2013.","title":"Selective search for object recognition","authors":"Uijlings","year":"2013","venue":"International journal of computer vision","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wan et al., 2018] Fang Wan, Pengxu Wei, Jianbin Jiao, Zhenjun Han, and Qixiang Ye. Min-entropy latent model for weakly supervised object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1297-1306, 2018.","title":"Min-entropy latent model for weakly supervised object detection","authors":"Fang Wan, Pengxu Wei, Jianbin Jiao, Zhenjun Han, Qixiang Ye","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2020] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. Deep high-resolution representation learning for visual recogni- tion. IEEE transactions on pattern analysis and machine intelligence, 43(10):3349-3364, 2020.","title":"Deep high-resolution representation learning for visual recognition","authors":"Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang","year":"2020","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2023] Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, et al. Review of large vision models and visual prompt engineering. arXiv preprint arXiv:2307.00855, 2023.","title":"Review of large vision models and visual prompt engineering","authors":"Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xu et al., 2022] Lian Xu, Wanli Ouyang, Mohammed Ben- namoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE Conference on Computer Vi- sion and Pattern Recognition, pages 4310-4319, 2022.","title":"Multi-class token transformer for weakly supervised semantic segmentation","authors":"Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Dan Xu","year":"2022","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2018] Yongqiang Zhang, Yancheng Bai, Min- gli Ding, Yongqiang Li, and Bernard Ghanem. W2f: A weakly-supervised to fully-supervised framework for ob- ject detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 928-936, 2018.","title":"W2f: A weakly-supervised to fully-supervised framework for object detection","authors":"Yongqiang Zhang, Yancheng Bai, Mingli Ding, Yongqiang Li, Bernard Ghanem","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2020] Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, and Yuchao Dai. Weakly-supervised salient object detection via scribble annotations. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 12546-12555, 2020.","title":"Weakly-supervised salient object detection via scribble annotations","authors":"Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, Yuchao Dai","year":"2020","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2022] Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung- Yeung Shum. Dino: Detr with improved denoising an- chor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605, 2022.","title":"Dino: Detr with improved denoising anchor boxes for end-to-end object detection","authors":"Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, Heung-Yeung Shum","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhou et al., 2016] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proceed- ings of the IEEE conference on computer vision and pat- tern recognition, pages 2921-2929, 2016.","title":"Learning deep features for discriminative localization","authors":"Zhou","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou et al., 2019] Yanning Zhou, Hao Chen, Jiaqi Xu, Qi Dou, and Pheng-Ann Heng. Irnet: Instance relation net- work for overlapping cervical cell segmentation. In Medi- cal Image Computing and Computer Assisted Intervention, pages 640-648. Springer, 2019.","title":"Irnet: Instance relation network for overlapping cervical cell segmentation","authors":"Yanning Zhou, Hao Chen, Jiaqi Xu, Qi Dou, Pheng-Ann Heng","year":"2019","venue":"Medical Image Computing and Computer Assisted Intervention","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}