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Lianli Gao

4 papers hereh-index 458 citations16 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.RO1
same name
  • Lianli Gao — 62 papers, h 50
  • Lianli Gao — 44 papers, h 0
  • Lianli Gao — 5 papers, h 3
  • Lianli Gao — 2 papers, h 6
  • Lianli Gao — 1 paper
  • Lianli Gao — 1 paper, h 1

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
20232026
most citedPart-Aware Transformer for Generalizable Person Re-identification

3 citations · 4 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2026

Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning

Cheng Chen, Pengpeng Zeng, Yuyu Guo +3

Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors (A and B), creating a composite update to t…

cs.RO2026

Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation

Junhong Zhu, Ji Zhang, Jingkuan Song +2

While generalist robot policies hold significant promise for learning diverse manipulation skills through imitation, their performance is often hindered by the long-tail distributi…

cs.CV2025★ 1 cited

Towards Generalized and Training-Free Text-Guided Semantic Manipulation

Yu Hong, Xiao Cai, Pengpeng Zeng +4

Text-guided semantic manipulation refers to semantically editing an image generated from a source prompt to match a target prompt, enabling the desired semantic changes (e.g., addi…

cs.CV2023★ 3 cited

Part-Aware Transformer for Generalizable Person Re-identification

Hao Ni, Yuke Li, Lianli Gao +2

Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. Vision Transformer usually yields better gen…

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