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Semantic segmentation is a key computer vision task that has been actively researched for decades.
Some methods for classification and analysis of multivariate observations
J. MacQueen · 1967
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The pascal visual object classes (voc) challenge
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Constrained convolutional neural networks for weakly supervised segmentation
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin P. Murphy, and Alan Loddon Yuille · 2018
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Few-shot semantic segmentation with prototype learning
Nanqing Dong and Eric P. Xing · 2018
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Zero-shot object detection: Learning to simultaneously recognize and localize novel concepts
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Conditional networks for few-shot semantic segmentation
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Ccnet: Criss-cross attention for semantic segmentation
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Invariant information clustering for unsupervised image classification and segmentation
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Grad-cam: Visual explanations from deep networks via gradient-based localization
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Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Jun Hao Liew, Sheng Tang, Steven C. H. Hoi, and Jiashi Feng · 2020
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Object-contextual representations for semantic segmentation
Yuhui Yuan, Xilin Chen, and Jingdong Wang · 2020
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Weakly-supervised salient object detection via scribble annotations
Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, and Yuchao Dai · 2020
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Sg-one: Similarity guidance network for one-shot semantic segmentation
Xiaolin Zhang, Yunchao Wei, Yi Yang, and Thomas Huang · 2020
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Transformer interpretability beyond attention visualization
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