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Hongbo Jiang

4 papers hereh-index 367 citations8 works total

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

author position
  • first author2
  • middle author2

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

fields
  • cs.CV3
  • cs.LG1
same name
  • Hongbo Jiang — 4 papers, h 5
  • Hongbo Jiang — 3 papers
  • Hongbo Jiang — 2 papers, h 40
  • Hongbo Jiang — 2 papers, h 16
  • Hongbo Jiang — 2 papers, h 34
  • Hongbo Jiang — 1 paper, 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

most citedElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning

1 citations · 1 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CV2026

Can Unified Generation and Understanding Models Maintain Semantic Equivalence Across Different Output Modalities?

Hongbo Jiang, Jie Li, Yunhang Shen +4

Unified Multimodal Large Language Models (U-MLLMs) integrate understanding and generation within a single architecture. However, existing evaluations typically assess these capabil…

cs.CV2026

Unleashing MLLMs on the Edge: A Unified Framework for Cross-Modal ReID via Adaptive SVD Distillation

Hongbo Jiang, Jie Li, Xinqi Cai +4

Practical cloud-edge deployment of Cross-Modal Re-identification (CM-ReID) faces challenges due to maintaining a fragmented ecosystem of specialized cloud models for diverse modali…

cs.CV2025

Insert Anything: Image Insertion via In-Context Editing in DiT

Wensong Song, Hong Jiang, Zongxing Yang +2

This work presents Insert Anything, a unified framework for reference-based image insertion that seamlessly integrates objects from reference images into target scenes under flexib…

cs.LG2025★ 1 cited

ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning

Huandong Chang, Zicheng Ma, Mingyuan Ma +4

Low-Rank Adaptation (LoRA) has become a widely adopted technique for fine-tuning large-scale pre-trained models with minimal parameter updates. However, existing methods rely on fi…

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