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Robust and accurate segmentation of scenes has become one core functionality in various visual recognition and navigation tasks.
Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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A category-level 3d object dataset: Putting the kinect to work
Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T Barron, Mario Fritz, Kate Saenko, and Trevor Darrell · 2013
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Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
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Cross modal distillation for supervision transfer
Saurabh Gupta, Judy Hoffman, and Jitendra Malik · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
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Mfnet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes
Qishen Ha, Kohei Watanabe, Takumi Karasawa, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture
Caner Hazirbas, Lingni Ma, Csaba Domokos, and Daniel Cremers · 2017
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Frustum pointnets for 3d object detection from rgb-d data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2018
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Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
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Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer · 2018
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Semantickitti: A dataset for semantic scene understanding of lidar sequences
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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More diverse means better: Multimodal deep learning meets remote-sensing imagery classification
Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, and Bing Zhang · 2020
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xmuda: Cross-modal unsupervised domain adaptation for 3d semantic segmentation
Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez · 2020
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2018 ieee grss data fusion challenge – fusion of multispectral lidar and hyperspectral data
Saurabh Prasad, Bertrand Le Saux, Naoto Yokoya, and Ronny Hansch · 2020
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Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal images
Johan Vertens, Jannik Zürn, and Wolfram Burgard · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Fps-net: A convolutional fusion network for large-scale lidar point cloud segmentation
Aoran Xiao, Xiaofei Yang, Shijian Lu, Dayan Guan, and Jiaxing Huang · 2021
Cited alongside, same era.
Multimodal knowledge expansion
Zihui Xue, Sucheng Ren, Zhengqi Gao, and Hang Zhao · 2021
Cited alongside, same era.
Perception-aware multi-sensor fusion for 3d lidar semantic segmentation
Zhuangwei Zhuang, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li, and Mingkui Tan · 2021
Cited alongside, same era.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Parameter efficient fine-tuning via cross block orchestration for segment anything model
Zelin Peng, Zhengqin Xu, Zhilin Zeng, Lingxi Xie, Qi Tian, and Wei Shen · 2023
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Sam-clip: Merging vision foundation models towards semantic and spatial understanding
Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari · 2023
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Efficientsam: Leveraged masked image pretraining for efficient segment anything
Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, et al · 2023
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Adaptformer: Adapting vision transformers for scalable visual recognition
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, and Ping Luo · 2022
Cited alongside, same era.
Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
Cited alongside, same era.
Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
Cited alongside, same era.
Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection
Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V Le, et al · 2022
Cited alongside, same era.
2023 ieee grss data fusion contest: Large-scale fine-grained building classification for semantic urban reconstruction
Claudio Persello; Ronny Hänsch; Gemine Vivone; Kaiqiang Chen; Zhiyuan Yan; Deke Tang; Hai Huang; Michael Schmitt; Xian Sun · 2022
Cited alongside, same era.
Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Cited alongside, same era.
Sam-adapter: Adapting segment anything in underperformed scenes
Tianrun Chen, Lanyun Zhu, Chaotao Deng, Runlong Cao, Yan Wang, Shangzhan Zhang, Zejian Li, Lingyun Sun, Ying Zang, and Papa Mao · 2023
Cited alongside, same era.
Side adapter network for open-vocabulary semantic segmentation
Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, and Xiang Bai · 2023
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Faster segment anything: Towards lightweight sam for mobile applications
Chaoning Zhang, Dongshen Han, Yu Qiao, Jung Uk Kim, Sung-Ho Bae, Seungkyu Lee, and Choong Seon Hong · 2023
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Haojie Zhang, Yongyi Su, Xun Xu, and Kui Jia · 2023
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Haojie Zhang, Yongyi Su, Xun Xu, and Kui Jia · 2023
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Personalize segment anything model with one shot
Renrui Zhang, Zhengkai Jiang, Ziyu Guo, Shilin Yan, Junting Pan, Hao Dong, Peng Gao, and Hongsheng Li · 2023
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Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, and Jinqiao Wang · 2023
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Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model
Keyan Chen, Chenyang Liu, Hao Chen, Haotian Zhang, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi · 2024
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Segment anything in high quality
Lei Ke, Mingqiao Ye, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu, et al · 2024
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Asam: Boosting segment anything model with adversarial tuning
Bo Li, Haoke Xiao, and Lv Tang · 2024
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Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang · 2024
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Ba-sam: Scalable bias-mode attention mask for segment anything model
Yiran Song, Qianyu Zhou, Xiangtai Li, Deng-Ping Fan, Xuequan Lu, and Lizhuang Ma · 2024
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Cat-sam: Conditional tuning for few-shot adaptation of segmentation anything model
Aoran Xiao, Weihao Xuan, Heli Qi, Yun Xing, Ruijie Ren, Xiaoqin Zhang, and Shijian Lu · 2024
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Weiming Zhang, Yexin Liu, Xu Zheng, and Lin Wang · 2024
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Uv-sam: Adapting segment anything model for urban village identification
Xin Zhang, Yu Liu, Yuming Lin, Qingmin Liao, and Yong Li · 2024
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