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Haoyang Liu

12 papers hereh-index 7180 citations28 works total

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

author position
  • sole author1
  • first author4
  • middle author6

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG4
  • cs.AI1
  • cs.CL1
  • math.NA1
  • q-bio.GN1
same name
  • Haoyang Liu — 11 papers, h 9
  • Haoyang Liu — 10 papers, h 8
  • Haoyang Liu — 9 papers, h 1
  • Haoyang Liu — 5 papers, h 3
  • Haoyang Liu — 5 papers, h 0
  • Haoyang Liu — 4 papers, 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
20232026
most citedToward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data

4 citations · 11 across the 11 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024★ 3 cited

Emotion Detection through Body Gesture and Face

Haoyang Liu

The project leverages advanced machine and deep learning techniques to address the challenge of emotion recognition by focusing on non-facial cues, specifically hands, body gesture…

cs.CV2024

Approximate Nullspace Augmented Finetuning for Robust Vision Transformers

Haoyang Liu, Aditya Singh, Yijiang Li +1

Enhancing the robustness of deep learning models, particularly in the realm of vision transformers (ViTs), is crucial for their real-world deployment. In this work, we provide a fi…

cs.CV2023

Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models

Tong Zhang, Haoyang Liu, Peiyan Zhang +2

In the field of computer graphics, the use of vector graphics, particularly Scalable Vector Graphics (SVG), represents a notable development from traditional pixel-based imagery. S…

cs.CV2023

Dataset Distillation via the Wasserstein Metric

Haoyang Liu, Yijiang Li, Tiancheng Xing +5

Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly re…

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