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Chunjiang Ge

8 papers hereh-index 101.3k citations11 works total

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

author position
  • first author2
  • middle author5

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV7
  • cs.CL1
same name
  • Chunjiang Ge — 3 papers, h 2
  • Chunjiang Ge — 3 papers, h 2

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
20212026
most citedCross-Modal Adapter for Vision-Language Retrieval

25 citations · 66 across the 7 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024★ 1 cited

Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment

Jiayi Guo, Junhao Zhao, Chaoqun Du +6

Test-time adaptation (TTA) aims to improve the performance of source-domain pre-trained models on previously unseen, shifted target domains. Traditional TTA methods primarily adapt…

cs.CV2024★ 1 cited

ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models

Chunjiang Ge, Sijie Cheng, Ziming Wang +6

High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadrat…

cs.CV2024★ 18 cited

Demystify Mamba in Vision: A Linear Attention Perspective

Dongchen Han, Ziyi Wang, Zhuofan Xia +7

Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision…

cs.CV2024★ 3 cited

LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images

Ruyi Xu, Yuan Yao, Zonghao Guo +7

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding the visual world. Conventional LMMs process images in fixed sizes and limited resolutions,…

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