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Yang Cong

18 papers hereh-index 314.1k citations108 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author18

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.LG5
  • cs.CR1
  • cs.RO1
same name
  • Yang Cong — 7 papers, h 5
  • Yang Cong — 6 papers, h 3
  • Yang Cong — 5 papers, h 2
  • Yang Cong — 4 papers, h 5
  • Yang Cong — 1 paper
  • Yang Cong — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedWhat Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation

21 citations · 68 across the 17 of their papers we have counts for

collaborators
Showing 2023 · cs.CVShow all

4 papers · 2 filters

cs.CV2023★ 3 cited

Heterogeneous Forgetting Compensation for Class-Incremental Learning

Jiahua Dong, Wenqi Liang, Yang Cong +1

Class-incremental learning (CIL) has achieved remarkable successes in learning new classes consecutively while overcoming catastrophic forgetting on old categories. However, most e…

cs.CV2023

Gradient-Semantic Compensation for Incremental Semantic Segmentation

Wei Cong, Yang Cong, Jiahua Dong +2

Incremental semantic segmentation aims to continually learn the segmentation of new coming classes without accessing the training data of previously learned classes. However, most…

cs.CV2023★ 1 cited

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

Jiahua Dong, Yang Cong, Gan Sun +4

3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D…

cs.CV2023

No One Left Behind: Real-World Federated Class-Incremental Learning

Jiahua Dong, Hongliu Li, Yang Cong +3

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.