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Liyan Chen

University of Texas at Austin

4 papers hereh-index 4275 citations8 works total

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

author position
  • first author1
  • middle author3

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

fields
  • cs.CV3
  • cs.AI1
affiliations
  • University of Texas at Austin
Homepage
same name
  • Liyan Chen — 4 papers, h 2
  • Liyan Chen — 3 papers, h 2
  • Liyan Chen — 2 papers, h 2
  • Liyan Chen — 1 paper, h 1
  • Liyan Chen — 1 paper, h 3
  • Liyan Chen — 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
20182023
most citedGeometric Pose Affordance: 3D Human Pose with Scene Constraints

41 citations · 49 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2023

Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation

Liyan Chen, Weihan Wang, Philippos Mordohai

We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the o…

cs.CV2020★ 8 cited

Improving VQA and its Explanations \\ by Comparing Competing Explanations

Jialin Wu, Liyan Chen, Raymond J. Mooney

Most recent state-of-the-art Visual Question Answering (VQA) systems are opaque black boxes that are only trained to fit the answer distribution given the question and visual conte…

cs.CV2019★ 41 cited

Geometric Pose Affordance: 3D Human Pose with Scene Constraints

Zhe Wang, Liyan Chen, Shaurya Rathore +2

Full 3D estimation of human pose from a single image remains a challenging task despite many recent advances. In this paper, we explore the hypothesis that strong prior information…

cs.AI2018

Embedding Multimodal Relational Data for Knowledge Base Completion

Pouya Pezeshkpour, Liyan Chen, Sameer Singh

Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simpl…

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