This content originally appeared on HackerNoon and was authored by Segmentation
:::info Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney.
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Table of Links
3. Method and 3.1. Problem definition
3.2. Baseline and 3.3. Uni-OVSeg framework
4. Experiments
6. Broader impacts and References
6. Broader impacts
The Uni-OVSeg framework represents a significant advancement in open-vocabulary segmentation by reducing the dependency on labour-intensive image-mask-text triplet annotations. This innovation has the potential to democratise access to cutting-edge vision perception systems, offering substantial benefits across various sectors, such as medical imaging and autonomous vehicles. The development of a more efficient and accurate vision perception system contributes to the community, potentially leading to more innovative applications and research in machine learning, computer vision, and related areas. As with any AI model, there is a risk of bias in the data used for training. Efforts must be made to ensure that the datasets are diverse and representative to avoid perpetuating or amplifying biases.
References
[1] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. NeurIPS, 35: 23716–23736, 2022. 2
\ [2] Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, and Tongliang Liu. Rsa: Reducing semantic shift from aggressive augmentations for self-supervised learning. NeurIPS, 35:21128–21141, 2022. 2
\ [3] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. ” O’Reilly Media, Inc.”, 2009. 7
\ [4] Daniel Bolya, Chong Zhou, Fanyi Xiao, and Yong Jae Lee. Yolact: Real-time instance segmentation. In ICCV, pages 9157–9166, 2019. 2
\ [5] Kaixin Cai, Pengzhen Ren, Yi Zhu, Hang Xu, Jianzhuang Liu, Changlin Li, Guangrun Wang, and Xiaodan Liang. Mixreorg: Cross-modal mixed patch reorganization is a good mask learner for open-world semantic segmentation. In ICCV, pages 1196–1205, 2023. 6
\ [6] Junbum Cha, Jonghwan Mun, and Byungseok Roh. Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs. In CVPR, pages 11165–11174, 2023. 2, 6
\ [7] Kai Chen, Jiangmiao Pang, Jiaqi Wang, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jianping Shi, Wanli Ouyang, et al. Hybrid task cascade for instance segmentation. In CVPR, pages 4974–4983, 2019. 2
\ [8] Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions. arXiv preprint arXiv:2311.12793, 2023. 5
\ [9] Ting Chen, Lala Li, Saurabh Saxena, Geoffrey Hinton, and David J Fleet. A generalist framework for panoptic segmentation of images and videos. In ICCV, pages 909–919, 2023. 2
\ [10] Xi Chen, Shuang Li, Ser-Nam Lim, Antonio Torralba, and Hengshuang Zhao. Open-vocabulary panoptic segmentation with embedding modulation. ICCV, 2023. 7
\ [11] Bowen Cheng, Maxwell D Collins, Yukun Zhu, Ting Liu, Thomas S Huang, Hartwig Adam, and Liang-Chieh Chen. Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. In CVPR, pages 12475– 12485, 2020. 2
\ [12] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Perpixel classification is not all you need for semantic segmentation. In NeurIPS, pages 17864–17875, 2021. 2
\ [13] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In CVPR, pages 1290–1299, 2022. 2, 3, 4, 5, 12
\ [14] Luca Ciampi, Carlos Santiago, Joao Paulo Costeira, Claudio ˜ Gennaro, and Giuseppe Amato. Domain adaptation for traffic density estimation. In VISIGRAPP (5: VISAPP), pages 185–195, 2021. 15
\ [15] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR, pages 3213–3223, 2016. 5
\ [16] Jian Ding, Nan Xue, Gui-Song Xia, and Dengxin Dai. Decoupling zero-shot semantic segmentation. In CVPR, pages 11583–11592, 2022. 2, 6
\ [17] Zheng Ding, Jieke Wang, and Zhuowen Tu. Openvocabulary universal image segmentation with maskclip. 2023. 3, 6, 7
\ [18] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88:303–338, 2010. 5
\ [19] Jean-Michel Fortin, Olivier Gamache, Vincent Grondin, Franc¸ois Pomerleau, and Philippe Giguere. Instance segmen- ` tation for autonomous log grasping in forestry operations. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6064–6071. IEEE, 2022. 15
\ [20] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, pages 3146–3154, 2019. 2
\ [21] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In ECCV, pages 540–557, 2022. 3, 5, 6
\ [22] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921, 2021. 5
\ [23] Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Gir- ´ shick. Mask r-cnn. In ICCV, pages 2961–2969, 2017. 2, 3
\ [24] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729–9738, 2020. 2
\ [25] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable ´ vision learners. In CVPR, pages 16000–16009, 2022. 2
\ [26] Jungseok Hong, Michael Fulton, and Junaed Sattar. Trashcan: A semantically-segmented dataset towards visual detection of marine debris. arXiv preprint arXiv:2007.08097, 2020. 15
\ [27] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan AllenZhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 13
\ [28] Yixiang Huang, Zhaoqing Wang, Xin Jiang, Ming Wu, Chuang Zhang, and Jun Guo. Pointshift: Point-wise shift mlp for pixel-level cloud type classification in meteorological satellite imagery. In IGARSS, pages 607–610. IEEE, 2022. 2
\ [29] Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Open clip, 2021. If you use this software, please cite it as below. 5
\ [30] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, pages 4904– 4916, 2021. 2, 3
\ [31] Richard M Karp, Umesh V Vazirani, and Vijay V Vazirani. An optimal algorithm for on-line bipartite matching. In STOC, pages 352–358, 1990. 5
\ [32] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 5
\ [33] Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollar. Panoptic segmentation. In ´ CVPR, pages 9404–9413, 2019. 2, 3
\ [34] 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. ICCV, 2023. 2, 3, 4, 5, 7, 14
\ [35] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 2, 6
\ [36] Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, and Jianfeng Gao. Semantic-sam: Segment and recognize anything at any granularity. arXiv preprint arXiv:2307.04767, 2023. 3
\ [37] Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, Ping Luo, and Tong Lu. Panoptic segformer: Delving deeper into panoptic segmentation with transformers. In CVPR, pages 1280–1289, 2022. 5
\ [38] Chen Liang, Wenguan Wang, Jiaxu Miao, and Yi Yang. Gmmseg: Gaussian mixture based generative semantic segmentation models. In NeurIPS, pages 31360–31375, 2022. 2
\ [39] Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. In CVPR, pages 7061–7070, 2023. 2, 6
\ [40] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C Lawrence ´ Zitnick. Microsoft coco: Common objects in context. In ECCV, pages 740–755, 2014. 5, 15
\ [41] Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, ´ Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In CVPR, pages 2117–2125, 2017. 12
\ [42] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. arXiv preprint arXiv:2304.08485, 2023. 2, 5
\ [43] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In CVPR, pages 11976–11986, 2022. 5
\ [44] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, pages 3431–3440, 2015. 2, 3
\ [45] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5
\ [46] Huaishao Luo, Junwei Bao, Youzheng Wu, Xiaodong He, and Tianrui Li. Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation. In ICML, pages 23033–23044, 2023. 3, 6
\ [47] Dantong Niu, Xudong Wang, Xinyang Han, Long Lian, Roei Herzig, and Trevor Darrell. Unsupervised universal image segmentation. arXiv preprint arXiv:2312.17243, 2023. 7
\ [48] 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 ICML, pages 8748–8763, 2021. 2, 3, 5
\ [49] Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, and Jonathon Shlens. Perceptual grouping in contrastive vision-language models. In ICCV, pages 5571–5584, 2023. 6
\ [50] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, pages 2556–2565, 2018. 2
\ [51] Corey Snyder and Minh Do. Streets: A novel camera network dataset for traffic flow. NeurIPS, 32, 2019. 15
\ [52] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in multimedia research. Communications of the ACM, 59(2):64–73, 2016. 2
\ [53] Zhi Tian, Chunhua Shen, and Hao Chen. Conditional convolutions for instance segmentation. In ECCV, pages 282–298, 2020. 2
\ [54] Zhi Tian, Bowen Zhang, Hao Chen, and Chunhua Shen. Instance and panoptic segmentation using conditional convolutions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):669–680, 2022. 2
\ [55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 30, 2017. 3, 4
\ [56] Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Axial-deeplab: Standalone axial-attention for panoptic segmentation. In ECCV, pages 108–126, 2020. 2
\ [57] Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Max-deeplab: End-to-end panoptic segmentation with mask transformers. In CVPR, pages 5463– 5474, 2021. 2
\ [58] Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari. Sam-clip: Merging vision foundation models towards semantic and spatial understanding. arXiv preprint arXiv:2310.15308, 2023. 6
\ [59] Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li. Solo: A simple framework for instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):8587–8601, 2021. 2
\ [60] Xudong Wang, Rohit Girdhar, Stella X Yu, and Ishan Misra. Cut and learn for unsupervised object detection and instance segmentation. In CVPR, pages 3124–3134, 2023. 7
\ [61] Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, and Tongliang Liu. Mosaic representation learning for self-supervised visual pre-training. In ICLR, 2022. 2
\ [62] Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, and Tongliang Liu. Exploring set similarity for dense self-supervised representation learning. In CVPR, pages 16590–16599, 2022. 2
\ [63] Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, and Tongliang Liu. Cris: Clip-driven referring image segmentation. In CVPR, pages 11686– 11695, 2022. 2
\ [64] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, pages 18134–18144, 2022. 2, 3, 6
\ [65] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Yi Wang, Yu Qiao, and Weidi Xie. Learning open-vocabulary semantic segmentation models from natural language supervision. In CVPR, pages 2935–2944, 2023. 2, 3, 6
\ [66] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. In CVPR, pages 2955–2966, 2023. 3, 5, 6, 7
\ [67] Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Han Hu, and Xiang Bai. A simple baseline for openvocabulary semantic segmentation with pre-trained visionlanguage model. In ECCV, pages 736–753, 2022. 2, 3, 6
\ [68] Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, and Xiang Bai. Side adapter network for open-vocabulary semantic segmentation. In CVPR, pages 2945–2954, 2023. 3, 6
\ [69] Lei Yang, Yan Zi Wei, Yisheng He, Wei Sun, Zhenhang Huang, Haibin Huang, and Haoqiang Fan. ishape: A first step towards irregular shape instance segmentation. arXiv preprint arXiv:2109.15068, 2021. 15
\ [70] Shuo Yang, Peize Sun, Yi Jiang, Xiaobo Xia, Ruiheng Zhang, Zehuan Yuan, Changhu Wang, Ping Luo, and Min Xu. Objects in semantic topology. In ICLR, 2021. 2
\ [71] Senthil Yogamani, Ciaran Hughes, Jonathan Horgan, Ganesh ´ Sistu, Padraig Varley, Derek O’Dea, Michal Uricar, Ste- ´ fan Milz, Martin Simon, Karl Amende, et al. Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9308–9318, 2019. 15
\ [72] Haoxuan You, Haotian Zhang, Zhe Gan, Xianzhi Du, Bowen Zhang, Zirui Wang, Liangliang Cao, Shih-Fu Chang, and Yinfei Yang. Ferret: Refer and ground anything anywhere at any granularity. arXiv preprint arXiv:2310.07704, 2023. 2
\ [73] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 2
\ [74] Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Cmt-deeplab: Clustering mask transformers for panoptic segmentation. In CVPR, pages 2560–2570, 2022. 2
\ [75] Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. k-means mask transformer. In ECCV, pages 288–307, 2022. 2
\ [76] Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and LiangChieh Chen. Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip. NeurIPS, 2023. 2, 3, 5, 6, 7
\ [77] Yuhui Yuan, Lang Huang, Jianyuan Guo, Chao Zhang, Xilin Chen, and Jingdong Wang. Ocnet: Object context for semantic segmentation. International Journal of Computer Vision, 129(8):2375–2398, 2021. 2
\ [78] Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, et al. Segvit: Semantic segmentation with plain vision transformers. In NeurIPS, pages 4971– 4982, 2022. 2
\ [79] Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal. Context encoding for semantic segmentation. In CVPR, pages 7151–7160, 2018. 2
\ [80] Hao Zhang, Feng Li, Xueyan Zou, Shilong Liu, Chunyuan Li, Jianwei Yang, and Lei Zhang. A simple framework for open-vocabulary segmentation and detection. In ICCV, pages 1020–1031, 2023. 3, 6
\ [81] Lingzhi Zhang, Shenghao Zhou, Simon Stent, and Jianbo Shi. Fine-grained egocentric hand-object segmentation: Dataset, model, and applications. In European Conference on Computer Vision, pages 127–145. Springer, 2022. 15
\ [82] Libo Zhang, Lutao Jiang, Ruyi Ji, and Heng Fan. Pidray: A large-scale x-ray benchmark for real-world prohibited item detection. International Journal of Computer Vision, 131 (12):3170–3192, 2023. 15
\ [83] Wenwei Zhang, Jiangmiao Pang, Kai Chen, and Chen Change Loy. K-net: Towards unified image segmentation. NeurIPS, 34:10326–10338, 2021. 2
\ [84] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In CVPR, pages 633–641, 2017. 5, 15
\ [85] Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. In ECCV, pages 696–712, 2022. 3
\ [86] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. In ICLR, 2020. 4
\ [87] Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, Jianfeng Wang, Lu Yuan, et al. Generalized decoding for pixel, image, and language. In CVPR, pages 15116–15127, 2023. 2, 6, 14, 15
\ [88] Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, Jianfeng Gao, and Yong Jae Lee. Segment everything everywhere all at once. arXiv preprint arXiv:2304.06718, 2023. 2
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Segmentation
Segmentation | Sciencx (2024-11-12T22:27:18+00:00) Uni-OVSeg: A Step Towards Efficient and Bias-Resilient Vision Systems. Retrieved from https://www.scien.cx/2024/11/12/uni-ovseg-a-step-towards-efficient-and-bias-resilient-vision-systems/
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