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researcher

Kaicheng yu

6 papers hereh-index 4100 citations7 works total

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

author position
  • middle author1
  • last author3

Across the 4 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.AI2
same name
  • Kaicheng Yu — 22 papers, h 20
  • Kaicheng Yu — 10 papers, h 6
  • Kaicheng Yu — 6 papers, h 4
  • Kaicheng Yu — 5 papers, h 3
  • Kaicheng Yu — 4 papers, h 3
  • Kaicheng Yu — 1 paper, h 0

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 citedM3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training

Zijia Song, Yelin Wang, Zhengyi Ma +5

With the advancement of artificial intelligence, research on oracle bone scripts has entered a new era. However, existing methods and benchmarks remain largely confined to recognit…

cs.CV2026

Channel-wise Vector Quantization

Wei Song, Tianhang Wang, Yitong Chen +5

We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantiza…

cs.CV2025

Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better

Dianyi Wang, Wei Song, Yikun Wang +4

Typical large vision-language models (LVLMs) apply autoregressive supervision solely to textual sequences, without fully incorporating the visual modality into the learning process…

cs.CV2025

DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies

Wei Song, Yuran Wang, Zijia Song +6

The differing representation spaces required for visual understanding and generation pose a challenge in unifying them within the autoregressive paradigm of large language models.…

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