◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Fanglin Chen

4 papers here

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
ORCID 0000-0002-9193-5412
same name
  • Fanglin Chen — 4 papers
  • Fanglin Chen — 1 paper
  • Fanglin Chen — 1 paper

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

most citedFew-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations

3 citations · 6 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2024

Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation

Jiapeng Su, Qi Fan, Guangming Lu +2

Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects of novel classes, supported by only a few annotated samples. However, existing FSS methods oft…

cs.CV2024

OrthCaps: An Orthogonal CapsNet with Sparse Attention Routing and Pruning

Xinyu Geng, Jiaming Wang, Jiawei Gong +4

Redundancy is a persistent challenge in Capsule Networks (CapsNet),leading to high computational costs and parameter counts. Although previous works have introduced pruning after t…

cs.CV2022★ 3 cited

Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations

Wenjie Pei, Shuang Wu, Dianwen Mei +3

While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specifi…

cs.CV2022★ 3 cited

Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection

Shuang Wu, Wenjie Pei, Dianwen Mei +3

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.