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computer-vision-10

Disjoint 15-5

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Dataset

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PASCAL VOC 2012

Libraries

Use these libraries to find disjoint-15-5-10 models and implementations

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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.

5196

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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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.

271 0

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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.

292 0

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Modeling the Background for Incremental Learning in Semantic Segmentation

S. R. Bulò, B. Caputo, E. Ricci, Fabio Cermelli, Massimiliano Mancini•Sun Feb 02 2020

This work revisits classical incremental learning methods, and proposes a new distillation-based framework which explicitly accounts for a semantic distribution shift, and introduces a novel strategy to initialize classifier's parameters, thus preventing biased predictions toward the background class.

344 0

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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.

119 0

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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.

127 0

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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.

12 0

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Disjoint 15-5 | State-of-the-Art