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researcher

Hao Fei

22 papers hereh-index 8594 citations20 works total

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

author position
  • first author2
  • middle author13
  • last author6

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CV19
  • cs.CL3
same name
  • Hao Fei — 48 papers, h 38
  • Hao Fei — 12 papers, h 6
  • Hao Fei — 10 papers, h 3
  • Hao Fei — 7 papers, h 7
  • Hao Fei — 7 papers, h 2
  • Hao Fei — 6 papers, h 5

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 citedVitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing

19 citations · 26 across the 21 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning

Shengqiong Wu, Hao Fei, Liangming Pan +3

Recent advancements in multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing various vision-language tasks. However, MLLMs face significant ch…

cs.CV2024★ 19 cited

Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing

Hao Fei, Shengqiong Wu, Hanwang Zhang +2

Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained inst…

cs.CL2024

Grammar Induction from Visual, Speech and Text

Yu Zhao, Hao Fei, Shengqiong Wu +3

Grammar Induction could benefit from rich heterogeneous signals, such as text, vision, and acoustics. In the process, features from distinct modalities essentially serve complement…

cs.CV2024★ 6 cited

OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding

Tao Zhang, Xiangtai Li, Hao Fei +5

Current universal segmentation methods demonstrate strong capabilities in pixel-level image and video understanding. However, they lack reasoning abilities and cannot be controlled…

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