computer-vision-1

Overlapped 15-1

3260 papers • 126 benchmarks • 313 datasets

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PASCAL VOC 2012

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Most implemented papers

Learning without Forgetting

Derek Hoiem, Zhizhong Li•Tue Jun 28 2016

This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques.

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Incremental Learning Techniques for Semantic Segmentation

Umberto Michieli, P. Zanuttigh•Tue Jul 30 2019

This work formally introduces the incremental learning problem for semantic segmentation in which a pixel-wise labeling is considered and proposes various approaches working both on the output logits and on intermediate features.

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PLOP: Learning without Forgetting for Continual Semantic Segmentation

M. Cord, Arthur Douillard, Yifu Chen, Arnaud Dapogny•Sun Nov 22 2020

Local POD is proposed, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level that significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks 1.

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Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation

M. Cord, Arthur Douillard, Yifu Chen, Arnaud Dapogny•Mon Jun 28 2021

Local POD is proposed, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level that significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks.

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SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

Y. Yoo, Sungmin Cha, Beomyoung Kim, Taesup Moon•Mon Jun 21 2021

This paper proposes a new method, dubbed SSUL-M (Semantic Segmentation with Unknown Label with Memory), by carefully combining techniques tailored for semantic segmentation by utilizing tiny exemplar memory for the first time in CISS to improve both plasticity and stability.

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Representation Compensation Networks for Continual Semantic Segmentation

Mingg-Ming Cheng, Xialei Liu, Chang-Bin Zhang, Jianqiang Xiao, Ying-Cong Chen•Wed Mar 09 2022

This work proposes to use a structural re-parameterization mechanism, named representation compensation (RC) module, to decouple the representation learning of both old and new knowledge, and outperforms state-of-the-art performance.

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SATS: Self-Attention Transfer for Continual Semantic Segmentation

Yiqiao Qiu, Yixing Shen, Zhuohao Sun, Yanchong Zheng, Xiaobin Chang, Weishi Zheng, Ruixuan Wang•Mon Mar 14 2022

Extensive evaluations on multiple public benchmarks support that the proposed self-attention transfer method can further effectively alleviate the catastrophic forgetting issue, and its flexible combination with one or more widely adopted strategies significantly outperforms state-of-the-art solutions.

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Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation

Joost van de Weijer, Dipam Goswami, D. Stricker, René Schuster•Wed Oct 12 2022

This work proposes to address the background shift with a novel classifier initialization method which employs gradient-based attribution to identify the most relevant weights for new classes from the classifier’s weights for the previous background and transfers these weights to the new classifier.

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Mitigating Background Shift in Class-Incremental Semantic Segmentation

WonJun Moon, Subeen Lee, Gilhan Park, Tae-Young Kim, Jae-Pil Heo•Mon Jul 15 2024

The proposed background-class separation framework for CISS encourages the separation between the background and new classes with a novel orthogonal objective along with label-guided output distillation and state-of-the-art results validate the effectiveness of these proposed methods.

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