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Yichen Li

18 papers here

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

author position
  • first author5
  • middle author12

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.SE9
  • cs.CV4
  • cs.LG3
  • cs.CL2
ORCID 0009-0009-8370-644X
same name
  • Yichen Li — 6 papers, h 6
  • Yichen Li — 6 papers
  • Yichen Li — 3 papers
  • Yichen Li — 3 papers
  • Yichen Li — 3 papers
  • Yichen Li — 2 papers

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
20212025
most citedFace It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs

25 citations · 47 across the 18 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

MultiModal Action Conditioned Video Generation

Yichen Li, Antonio Torralba

Current video models fail as world model as they lack fine-graiend control. General-purpose household robots require real-time fine motor control to handle delicate tasks and urgen…

cs.CV2025

MAFE R-CNN: Selecting More Samples to Learn Category-aware Features for Small Object Detection

Yichen Li, Qiankun Liu, Zhenchao Jin +3

Small object detection in intricate environments has consistently represented a major challenge in the field of object detection. In this paper, we identify that this difficulty st…

cs.CV2024

Technique Report of CVPR 2024 PBDL Challenges

Ying Fu, Yu Li, Shaodi You +96

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…

cs.CV2023★ 1 cited

Improving the Transferability of Adversarial Samples by Path-Augmented Method

Jianping Zhang, Jen-tse Huang, Wenxuan Wang +5

Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon…

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