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Han Fang

12 papers hereh-index 595 citations19 works total

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

author position
  • first author1
  • middle author9

Across the 10 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.DC1
same name
  • Han Fang — 27 papers, h 18
  • Han Fang — 14 papers, h 8
  • Han Fang — 12 papers, h 8
  • Han Fang — 10 papers, h 3
  • Han Fang — 6 papers, h 3
  • Han Fang — 5 papers, h 3

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 citedTrusted Unified Feature-Neighborhood Dynamics for Multi-View Classification

1 citations · 1 across the 10 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.DC2026

Understanding NVIDIA Grace-Blackwell Unified Memory Through Placement-Controlled Microbenchmarks

Nathan Graddon, Aaron Jarmusch, Sunita Chandrasekaran

NVIDIA Grace--Blackwell systems make CPU--GPU memory increasingly unified at the programming level, but unified addressability does not make the memory system uniform. A CUDA manag…

cs.CV2026

MeSD: Multi-Evidence Self-Distillation for VideoLLM

Weijie Zhu, Han Fang, Hanyu Fu +13

While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-level guidance. On-policy self-…

cs.CV2026

GeoAnchor: Collaborative Reasoning via Latent Decomposition for 3D Spatial Understanding

Hao Li, Han Fang, Zixin Pan +8

Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a critical challenge. Existing me…

cs.CV2026

Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding

Tianyi Gao, Han Fang, Tianyi Ding +9

Multimodal Large Language Models have made great progress in grounding tasks, yet existing methods still struggle to unify precise localization and complex reasoning. For one thing…

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