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Qiming Zhang

25 papers hereh-index 173.1k citations41 works total

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

author position
  • first author4
  • middle author21

Across the 25 of 25 papers where every author was matched, so the position is known.

fields
  • cs.CV22
  • cs.AI3
same name
  • Qiming Zhang — 7 papers, h 4
  • Qiming Zhang — 5 papers, h 4
  • Qiming Zhang — 4 papers, h 5
  • Qiming Zhang — 4 papers, h 3
  • Qiming Zhang — 2 papers, h 1
  • Qiming Zhang — 1 paper, 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
20192026
most citedViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

353 citations · 791 across the 25 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.AI2026

Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI

Kairos Team, Fei Wang, Shan You +21

We introduce \textbf{Kairos}, a regret-aware native world-action model stack for Physical AI. Kairos is motivated by the view that a physical world model should not aim to fully si…

cs.AI2026

Seirênes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning

Chi Zhang, Haibo Qiu, Qiming Zhang +3

We present Seirênes, a self-play RL framework that transforms contextual interference from a failure mode of LLM reasoning into an internal training signal for co-evolving more res…

cs.CV2026

Na-IRSTD: Enhancing Infrared Small Target Detection via Native-Resolution Feature Selection and Fusion

Qian Xu, Chi Zhang, Qiming Zhang +3

Infrared small target detection (IRSTD) faces the inherent challenge of precisely localizing dim targets amid complex background clutter. While progress has been made, existing met…

cs.CV2026

Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation

Jiawei Zhou, Chi Zhang, Xiang Feng +6

We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executa…

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