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Wei Shi

10 papers hereh-index 7337 citations13 works total

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

author position
  • middle author9

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.CL3
  • cs.AI1
same name
  • Wei Shi — 21 papers, h 47
  • Wei Shi — 15 papers, h 19
  • Wei Shi — 8 papers, h 6
  • Wei Shi — 8 papers, h 4
  • Wei Shi — 7 papers, h 3
  • Wei Shi — 5 papers, h 12

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
20242026
most citedGecko: Versatile Text Embeddings Distilled from Large Language Models

11 citations · 12 across the 9 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark

Bin Shan, Xiang Fei, Wei Shi +6

The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchma…

cs.CV2024

ParGo: Bridging Vision-Language with Partial and Global Views

An-Lan Wang, Bin Shan, Wei Shi +7

This work presents ParGo, a novel Partial-Global projector designed to connect the vision and language modalities for Multimodal Large Language Models (MLLMs). Unlike previous work…

cs.CV2024★ 1 cited

TextSquare: Scaling up Text-Centric Visual Instruction Tuning

Jingqun Tang, Chunhui Lin, Zhen Zhao +15

Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of lea…

cs.CL2024★ 11 cited

Gecko: Versatile Text Embeddings Distilled from Large Language Models

Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren +17

We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language model…

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