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Liang Wang

Institute of Automation, Chinese Academy of Sciences

48 papers hereh-index 16688 citations64 works total

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

author position
  • first author1
  • middle author7
  • last author28

Across the 36 of 48 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.LG10
  • cs.AI7
  • cs.CV7
  • cs.IR4
  • cs.CR2
affiliations
  • Institute of Automation, Chinese Academy of Sciences
same name
  • Liang Wang — 72 papers, h 79
  • Liang Wang — 40 papers, h 94
  • Liang Wang — 31 papers, h 12
  • Liang Wang — 27 papers, h 22
  • Liang Wang — 22 papers, h 14
  • Liang Wang — 18 papers, h 4

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 citedDiffusion Models for Molecules: A Survey of Methods and Tasks

6 citations · 40 across the 46 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

3 papers · 2 filters

cs.CV2024

Beyond Filtering: Adaptive Image-Text Quality Enhancement for MLLM Pretraining

Han Huang, Yuqi Huo, Zijia Zhao +6

Multimodal large language models (MLLMs) have made significant strides by integrating visual and textual modalities. A critical factor in training MLLMs is the quality of image-tex…

cs.CV2024

Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization

Huanhuan Ma, Jinghao Zhang, Qiang Liu +2

The rapid spread of information through mobile devices and media has led to the widespread of false or deceptive news, causing significant concerns in society. Among different type…

cs.CV2024

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

Junfei Wu, Qiang Liu, Ding Wang +4

Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that t…

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