{"type":"ScholarlyArticle","title":"PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation","authors":[{"name":"Deyi Ji","slug":"deyi-ji","given":"Deyi","family":"Ji","affiliation":"University of Science and Technology of China; Alibaba Group"},{"name":"Wenwei Jin","slug":"wenwei-jin","given":"Wenwei","family":"Jin","affiliation":"Alibaba Group"},{"name":"Hongtao Lu","slug":"hongtao-lu","given":"Hongtao","family":"Lu","affiliation":"Dept. of CSE , MOE Key Lab of Artificial Intelligence , AI Institute , Shanghai Jiao Tong University"},{"name":"Feng Zhao","slug":"feng-zhao","given":"Feng","family":"Zhao","affiliation":"University of Science and Technology of China"}],"abstract":"The ascension of Unmanned Aerial Vehicles (UAVs) in various fields necessitates effective UAV image segmentation, which faces challenges due to the dynamic perspectives of UAV-captured images. Traditional segmentation algorithms falter as they cannot accurately mimic the complexity of UAV perspectives, and the cost of obtaining multi-perspective labeled datasets is prohibitive. To address these issues, we introduce the PPTFormer, a novel Pseudo Multi-Perspective Transformer network that revolutionizes UAV image segmentation. Our approach circumvents the need for actual multi-perspective data by creating pseudo perspectives for enhanced multi-perspective learning. The PPTFormer network boasts Perspective Decomposition, novel Perspective Prototypes, and a specialized encoder and decoder that together achieve superior segmentation results through Pseudo Multi-Perspective Attention  (PMP Attention) and fusion. Our experiments demonstrate that PPTFormer achieves state-of-the-art performance across five UAV segmentation datasets, confirming its capability to effectively simulate UAV flight perspectives and significantly advance segmentation precision. This work presents a pioneering leap in UAV scene understanding and sets a new benchmark for future developments in semantic segmentation.","doi":"10.24963/ijcai.2024/99","url":"https://ijcai.org/proceedings/2024/99","pdf":"https://ijcai.org/proceedings/2024/0099.pdf","markdown":"https://ijcai.org/proceedings/2024/99.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/99","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":"Computer Vision","track":"Main Track","number":99,"pages":{"first":893,"last":901},"keywords":["Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Bamberger and Smith, 1992] R.H. Bamberger and M.J.T. Smith. A filter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing, 40(4):882-893, 1992.","title":"A filter bank for the directional decomposition of images: theory and design","authors":"R H Bamberger, M J T Smith","year":"1992","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Burt and Adelson, 1983] P. Burt and E. Adelson. The lapla- cian pyramid as a compact image code. IEEE Transactions on Communications, 31(4):532-540, 1983.","title":"The laplacian pyramid as a compact image code","authors":"P Burt, E Adelson","year":"1983","venue":"IEEE Transactions on Communications","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2023] Tianrun Chen, Lanyun Zhu, Chaotao Ding, Runlong Cao, Shangzhan Zhang, Yan Wang, Zejian Li, Lingyun Sun, Papa Mao, and Ying Zang. Sam fails to segment anything?-sam-adapter: Adapting sam in under- performed scenes: Camouflage, shadow, and more. arXiv preprint arXiv:2304.09148, 2023.","title":"Sam fails to segment anything?-sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, and more","authors":"Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2024a] Tianrun Chen, Chaotao Ding, Lanyun Zhu, Tao Xu, Deyi Ji, Ying Zang, and Zejian Li. xlstm- unet can be an effective 2d\\& 3d medical image segmenta- tion backbone with vision-lstm (vil) better than its mamba counterpart. arXiv preprint arXiv:2407.01530, 2024.","title":"xlstmunet can be an effective 2d\\& 3d medical image segmentation backbone with vision-lstm (vil) better than its mamba counterpart","authors":"Chen","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2024b] Tianrun Chen, Chunan Yu, Jing Li, Jianqi Zhang, Lanyun Zhu, Deyi Ji, Yong Zhang, Ying Zang, Zejian Li, and Lingyun Sun. Reason- ing3d -grounding and reasoning in 3d: Fine-grained zero-shot open-vocabulary 3d reasoning part segmenta- tion via large vision-language models. arXiv preprint arXiv:2405.19326, 2024.","title":"Reasoning3d -grounding and reasoning in 3d: Fine-grained zero-shot open-vocabulary 3d reasoning part segmentation via large vision-language models","authors":"Chen","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Do and Vetterli, 2005] M.N. Do and M. Vetterli. The con- tourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Pro- cessing, 14(12):2091-2106, 2005.","title":"The contourlet transform: an efficient directional multiresolution image representation","authors":"M N Do, M Vetterli","year":"2005","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Feng et al., 2018] Weitao Feng, Deyi Ji, Yiru Wang, Shuorong Chang, Hansheng Ren, and Weihao Gan. Chal- lenges on large scale surveillance video analysis. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 69-76, 2018.","title":"Challenges on large scale surveillance video analysis","authors":"Feng","year":"2018","venue":"IEEE Conference on Computer Vision and Pattern Recognition Workshops","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hu et al., 2020] Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, and Junjie Yan. Class-wise dynamic graph convolution for semantic segmentation. In European Con- ference on Computer Vision, pages 1-17, 2020.","title":"Class-wise dynamic graph convolution for semantic segmentation","authors":"Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, Junjie Yan","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ji et al., 2019] Deyi Ji, Hongtao Lu, and Tongzhen Zhang. End to end multi-scale convolutional neural network for crowd counting. In Eleventh International Conference on Machine Vision, volume 11041, pages 761-766, 2019.","title":"End to end multi-scale convolutional neural network for crowd counting","authors":"Deyi Ji, Hongtao Lu, Tongzhen Zhang","year":"2019","venue":"Eleventh International Conference on Machine Vision","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ji et al., 2020] Deyi Ji, Haoran Wang, Hanzhe Hu, Weihao Gan, Wei Wu, and Junjie Yan. Context-aware graph convo- lution network for target re-identification. arXiv preprint arXiv:2012.04298, 2020.","title":"Context-aware graph convolution network for target re-identification","authors":"Deyi Ji, Haoran Wang, Hanzhe Hu, Weihao Gan, Wei Wu, Junjie Yan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ji et al., 2022] Deyi Ji, Haoran Wang, Mingyuan Tao, Jian- qiang Huang, Xian-Sheng Hua, and Hongtao Lu. Struc- tural and statistical texture knowledge distillation for semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16876- 16885, 2022.","title":"Structural and statistical texture knowledge distillation for semantic segmentation","authors":"Deyi Ji, Haoran Wang, Mingyuan Tao, Jianqiang Huang, Xian-Sheng Hua, Hongtao Lu","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ji et al., 2023a] Deyi Ji, Siqi Gao, Mingyuan Tao, Hong- tao Lu, and Feng Zhao. Changenet: Multi-temporal asymmetric change detection dataset. arXiv preprint arXiv:2312.17428, 2023.","title":"Changenet: Multi-temporal asymmetric change detection dataset","authors":"Ji","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ji et al., 2023b] Deyi Ji, Feng Zhao, and Hongtao Lu. Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation. Inter- national Joint Conference on Artificial Intelligence, pages 920-928, 2023.","title":"Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation","authors":"Ji","year":"2023","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2023/102"},{"position":14,"raw":"[Ji et al., 2023c] Deyi Ji, Feng Zhao, Hongtao Lu, Mingyuan Tao, and Jieping Ye. Ultra-high resolution segmentation with ultra-rich context: A novel benchmark. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23621-23630, June 2023.","title":"Ultra-high resolution segmentation with ultra-rich context: A novel benchmark","authors":"Ji","year":"2023","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ji et al., 2024a] Deyi Ji, Siqi Gao, Lanyun Zhu, Qi Zhu, Yiru Zhao, Peng Xu, Hongtao Lu, Feng Zhao, and Jieping Ye. View-centric multi-object tracking with ho- mographic matching in moving uav. arXiv preprint arXiv:2403.10830, 2024.","title":"View-centric multi-object tracking with homographic matching in moving uav","authors":"Ji","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ji et al., 2024b] Deyi Ji, Feng Zhao, Lanyun Zhu, Wenwei Jin, Hongtao Lu, and Jieping Ye. Discrete latent perspec- tive learning for segmentation and detection. International Conference on Machine Learning, 2024.","title":"Discrete latent perspective learning for segmentation and detection","authors":"Ji","year":"2024","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2021] Xiangtai Li, Hao He, Xia Li, Duo Li, Guan- gliang Cheng, Jianping Shi, Lubin Weng, Yunhai Tong, and Zhouchen Lin. Pointflow: Flowing semantics through points for aerial image segmentation. In IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 4217-4226, 2021.","title":"Pointflow: Flowing semantics through points for aerial image segmentation","authors":"Xiangtai Li, Hao He, Xia Li, Duo Li, Guangliang Cheng, Jianping Shi, Lubin Weng, Yunhai Tong, Zhouchen Lin","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"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 IEEE/CVF 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":"IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Long et al., 2015] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for seman- tic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3431-3440, 2015.","title":"Fully convolutional networks for semantic segmentation","authors":"Jonathan Long, Evan Shelhamer, Trevor Darrell","year":"2015","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2021a] Haoran Wang, Licheng Jiao, Fang Liu, Lingling Li, Xu Liu, Deyi Ji, and Weihao Gan. Ipgn: Inter- activeness proposal graph network for human-object inter- action detection. IEEE Transactions on Image Processing, 30:6583-6593, 2021.","title":"Ipgn: Interactiveness proposal graph network for human-object interaction detection","authors":"Wang","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2021b] Haoran Wang, Licheng Jiao, Fang Liu, Lingling Li, Xu Liu, Deyi Ji, and Weihao Gan. Learn- ing social spatio-temporal relation graph in the wild and a video benchmark. IEEE Transactions on Neural Networks and Learning Systems, 34(6):2951-2964, 2021.","title":"Learning social spatio-temporal relation graph in the wild and a video benchmark","authors":"Wang","year":"2021","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Xie et al., 2021] Enze Xie, Wenhai Wang, Zhiding Yu, An- ima Anandkumar, Jose M Alvarez, and Ping Luo. Seg- former: Simple and efficient design for semantic segmen- tation with transformers. In 35th Conference on Neural Information Processing Systems, pages 1-13, 2021.","title":"Segformer: Simple and efficient design for semantic segmentation with transformers","authors":"Xie","year":"2021","venue":"35th Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang and Ma, 2022] Fengyu Yang and Chenyang Ma. Sparse and complete latent organization for geospatial se- mantic segmentation. In IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 1809-1818, 2022.","title":"Sparse and complete latent organization for geospatial semantic segmentation","authors":"Yang, Ma ;, Fengyu Yang, Chenyang Ma","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yang et al., 2022a] Zizheng Yang, Xin Jin, Kecheng Zheng, and Feng Zhao. Unleashing potential of unsupervised pre- training with intra-identity regularization for person re- identification. In IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 14298-14307, 2022.","title":"Unleashing potential of unsupervised pretraining with intra-identity regularization for person reidentification","authors":"Yang","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yang et al., 2022b] Zizheng Yang, Mingde Yao, Jie Huang, Man Zhou, and Feng Zhao. Sir-former: Stereo image restoration using transformer. In ACM international con- ference on multimedia, pages 6377-6385, 2022.","title":"Sir-former: Stereo image restoration using transformer","authors":"Yang","year":"2022","venue":"ACM international conference on multimedia","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Yang et al., 2023] Zizheng Yang, Jie Huang, Jiahao Chang, Man Zhou, Hu Yu, Jinghao Zhang, and Feng Zhao. Visual recognition-driven image restoration for multiple degra- dation with intrinsic semantics recovery. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14059-14070, 2023.","title":"Visual recognition-driven image restoration for multiple degradation with intrinsic semantics recovery","authors":"Zizheng Yang, Jie Huang, Jiahao Chang, Man Zhou, Hu Yu, Jinghao Zhang, Feng Zhao","year":"2023","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yang et al., 2024] Zizheng Yang, Jie Huang, Man Zhou, Naishan Zheng, and Feng Zhao. IRVR: A general image restoration framework for visual recognition. IEEE Trans- actions on Multimedia, pages 7012-7026, 2024.","title":"IRVR: A general image restoration framework for visual recognition","authors":"Zizheng Yang, Jie Huang, Man Zhou, Naishan Zheng, Feng Zhao","year":"2024","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yu et al., 2022a] Hu Yu, Jie Huang, Feng Zhao, Jinwei Gu, Chen Change Loy, Deyu Meng, Chongyi Li, et al. Deep fourier up-sampling. Advances in Neural Information Pro- cessing Systems, 35:22995-23008, 2022.","title":"Deep fourier up-sampling","authors":"Yu","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Yu et al., 2022b] Hu Yu, Naishan Zheng, Man Zhou, Jie Huang, Zeyu Xiao, and Feng Zhao. Frequency and spatial dual guidance for image dehazing. In European Confer- ence on Computer Vision, pages 181-198. Springer, 2022.","title":"Frequency and spatial dual guidance for image dehazing","authors":"Yu","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yu et al., 2022c] Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, and Shuicheng Yan. Metaformer is actually what you need for vision. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10819-10829, 2022.","title":"Metaformer is actually what you need for vision","authors":"Yu","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yu et al., 2023] Wei Yu, Qi Zhu, Naishan Zheng, Jie Huang, Man Zhou, and Feng Zhao. Learning non-uniform- sampling for ultra-high-definition image enhancement. In ACM International Conference on Multimedia, pages 1412-1421, 2023.","title":"Learning non-uniformsampling for ultra-high-definition image enhancement","authors":"Wei Yu, Qi Zhu, Naishan Zheng, Jie Huang, Man Zhou, Feng Zhao","year":"2023","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yu et al., 2024] Hu Yu, Jie Huang, Lingzhi Li, Feng Zhao, et al. Deep fractional fourier transform. Advances in Neu- ral Information Processing Systems, 2024.","title":"Deep fractional fourier transform","authors":"Hu Yu, Jie Huang, Lingzhi Li, Feng Zhao","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zheng et al., 2020] Zhuo Zheng, Yanfei Zhong, Junjue Wang, and Ailong Ma. Foreground-aware relation network for geospatial object segmentation in high spatial resolu- tion remote sensing imagery. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4096- 4105, 2020.","title":"Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery","authors":"Zhuo Zheng, Yanfei Zhong, Junjue Wang, Ailong Ma","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zheng et al., 2021] Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, and Li Zhang. Rethinking semantic segmentation from a sequence-to- sequence perspective with transformers. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1-10, 2021.","title":"Rethinking semantic segmentation from a sequence-tosequence perspective with transformers","authors":"Zheng","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zheng et al., 2023a] Naishan Zheng, Jie Huang, Feng Zhao, Xueyang Fu, and Feng Wu. Unsupervised underexposed image enhancement via self-illuminated and perceptual guidance. IEEE Transactions on Multimedia, 2023.","title":"Unsupervised underexposed image enhancement via self-illuminated and perceptual guidance","authors":"Zheng","year":"2023","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zheng et al., 2023b] Naishan Zheng, Jie Huang, Man Zhou, Zizheng Yang, Qi Zhu, and Feng Zhao. Learning semantic degradation-aware guidance for recognition-driven unsu- pervised low-light image enhancement. In AAAI Confer- ence on Artificial Intelligence, pages 3678-3686, 2023.","title":"Learning semantic degradation-aware guidance for recognition-driven unsupervised low-light image enhancement","authors":"Zheng","year":"2023","venue":"AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhou et al., 2023] Man Zhou, Naishan Zheng, Yuan Xu, Chun-Le Guo, and Chongyi Li. Training your image restoration network better with random weight network as optimization function. In 37th Advances in Neural Infor- mation Processing Systems, pages 1270-1282, 2023.","title":"Training your image restoration network better with random weight network as optimization function","authors":"Zhou","year":"2023","venue":"37th Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhu et al., 2021] Lanyun Zhu, Deyi Ji, Shiping Zhu, Wei- hao Gan, Wei Wu, and Junjie Yan. Learning statistical texture for semantic segmentation. In IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 12537-12546, 2021.","title":"Learning statistical texture for semantic segmentation","authors":"Lanyun Zhu, Deyi Ji, Shiping Zhu, Weihao Gan, Wei Wu, Junjie Yan","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhu et al., 2023a] Lanyun Zhu, Tianrun Chen, Deyi Ji, Jieping Ye, and Jun Liu. Llafs: When large-language models meet few-shot segmentation. arXiv preprint arXiv:2311.16926, 2023.","title":"Llafs: When large-language models meet few-shot segmentation","authors":"Zhu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhu et al., 2023b] Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, and Jun Liu. Continual semantic seg- mentation with automatic memory sample selection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3082-3092, 2023.","title":"Continual semantic segmentation with automatic memory sample selection","authors":"Zhu","year":"2023","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhu et al., 2023c] Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, and Jun Liu. Learning gabor texture fea- tures for fine-grained recognition. In IEEE/CVF Interna- tional Conference on Computer Vision, pages 1621-1631, 2023.","title":"Learning gabor texture features for fine-grained recognition","authors":"Zhu","year":"2023","venue":"IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhu et al., 2023d] Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, and Jun Liu. Learning gabor texture fea- tures for fine-grained recognition. In IEEE/CVF Interna- tional Conference on Computer Vision, pages 1621-1631, 2023.","title":"Learning gabor texture features for fine-grained recognition","authors":"Zhu","year":"2023","venue":"IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhu et al., 2023e] Qi Zhu, Jie Huang, Naishan Zheng, Hongzhi Gao, Chongyi Li, Yuan Xu, Feng Zhao, et al. Fouridown: Factoring down-sampling into shuffling and superposing. In Advances in Neural Information Process- ing Systems, volume 36, pages 1-14, 2023.","title":"Fouridown: Factoring down-sampling into shuffling and superposing","authors":"Zhu","year":"2023","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhu et al., 2023f] Qi Zhu, Man Zhou, Naishan Zheng, Chongyi Li, Jie Huang, and Feng Zhao. Exploring tem- poral frequency spectrum in deep video deblurring. In IEEE/CVF International Conference on Computer Vision, pages 12428-12437, 2023.","title":"Exploring temporal frequency spectrum in deep video deblurring","authors":"Zhu","year":"2023","venue":"IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhu et al., 2024a] Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, and Jun Liu. Addressing background con- text bias in few-shot segmentation through iterative mod- ulation. In IEEE/CVF International Conference on Com- puter Vision, pages 1-10, 2024.","title":"Addressing background context bias in few-shot segmentation through iterative modulation","authors":"Zhu","year":"2024","venue":"IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhu et al., 2024b] Lanyun Zhu, Deyi Ji, Tianrun Chen, Peng Xu, Jieping Ye, and Jun Liu. Ibd: Alleviating halluci- nations in large vision-language models via image-biased decoding. arXiv preprint arXiv:2402.18476, 2024.","title":"Ibd: Alleviating hallucinations in large vision-language models via image-biased decoding","authors":"Zhu","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}