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Hao Fang

4 papers hereh-index 458 citations7 works total

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

author position
  • middle author4

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

fields
  • cs.CV3
  • cs.AI1
same name
  • Hao Fang — 26 papers, h 10
  • Hao Fang — 11 papers, h 4
  • Hao Fang — 5 papers, h 2
  • Hao Fang — 3 papers, h 5
  • Hao Fang — 3 papers, h 3
  • Hao Fang — 2 papers, h 0

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

collaborators

4 papers

cs.AI2026

TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning

Mingzu Liu, Hao Fang, Runmin Cong

Fine-Tuning-as-a-Service (FTaaS) facilitates the customization of Multimodal Large Language Models (MLLMs) but introduces critical backdoor risks via poisoned data. Existing defens…

cs.CV2026

RSONet: Region-guided Selective Optimization Network for RGB-T Salient Object Detection

Bin Wan, Runmin Cong, Xiaofei Zhou +3

This paper focuses on the inconsistency in salient regions between RGB and thermal images. To address this issue, we propose the Region-guided Selective Optimization Network for RG…

cs.CV2025

Empowering DINO Representations for Underwater Instance Segmentation via Aligner and Prompter

Zhiyang Chen, Chen Zhang, Hao Fang +1

Underwater instance segmentation (UIS), integrating pixel-level understanding and instance-level discrimination, is a pivotal technology in marine resource exploration and ecologic…

cs.CV2025

UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken

Runmin Cong, Zongji Yu, Hao Fang +2

Underwater Instance Segmentation (UIS) tasks are crucial for underwater complex scene detection. Mamba, as an emerging state space model with inherently linear complexity and globa…

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