computer-vision-10

Disjoint 10-1

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in disjoint-10-1-10

Trend

Dataset

Best Model

Actions

PASCAL VOC 2012

Libraries

i

Use these libraries to find disjoint-10-1-10 models and implementations

Datasets

No datasets available.

Subtasks

No subtasks available.

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

Content

0

Paper Graph

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

Paper Graph

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

Paper Graph

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

Paper Graph

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

Paper Graph

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

Paper Graph

Adding a benchmark result helps the community track progress.