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researcher

Hao Chen

13 papers hereh-index 6162 citations13 works total

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

author position
  • middle author10
  • last author1

Across the 11 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV10
  • cs.CL3
same name
  • Hao Chen — 77 papers, h 63
  • Hao Chen — 33 papers, h 24
  • Hao Chen — 25 papers, h 13
  • Hao Chen — 24 papers, h 11
  • Hao Chen — 24 papers, h 12
  • Hao Chen — 20 papers, h 8

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
20242026
most citedA Simple Image Segmentation Framework via In-Context Examples

2 citations · 2 across the 11 of their papers we have counts for

collaborators
Showing 2025 · cs.CVShow all

4 papers · 2 filters

cs.CV2025

Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality

Zekai Luo, Zongze Du, Zhouhang Zhu +7

Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a signific…

cs.CV2025

Unified Open-World Segmentation with Multi-Modal Prompts

Yang Liu, Yufei Yin, Chenchen Jing +7

In this work, we present COSINE, a unified open-world segmentation model that consolidates open-vocabulary segmentation and in-context segmentation with multi-modal prompts (e.g.,…

cs.CV2025

Learning by Imagining: Debiased Feature Augmentation for Compositional Zero-Shot Learning

Haozhe Zhang, Chenchen Jing, Mingyu Liu +2

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions by learning prior knowledge of seen primitives, \textit{i.e.}, attributes and objects…

cs.CV2025

PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training

Cong Chen, Mingyu Liu, Chenchen Jing +5

This paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs) particularly for dense image captioning tasks. To tackle the challenge, we id…

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