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Ming-Ching Chang

7 papers hereh-index 383 citations17 works total

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

author position
  • middle author6
  • last author1

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

fields
  • cs.CV5
  • cs.CL1
  • cs.LG1
same name
  • Ming-Ching Chang — 20 papers, h 18
  • Ming-Ching Chang — 11 papers, h 4
  • Ming-Ching Chang — 8 papers, h 25
  • Ming-Ching Chang — 8 papers, h 4
  • Ming-Ching Chang — 4 papers, h 4
  • Ming-Ching Chang — 2 papers, h 6

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
collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2026

The 10th AI City Challenge

Zheng Tang, Shuo Wang, David C. Anastasiu +34

The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with v…

cs.CV2026

Partial Ring Scan: Revisiting Scan Order in Vision State Space Models

Yi-Kuan Hsieh, Kuan-Chuan Peng, Xin li +3

State Space Models (SSMs) have emerged as efficient alternatives to attention for vision tasks, offering lineartime sequence processing with competitive accuracy. Vision SSMs, howe…

cs.CV2025

The 9th AI City Challenge

Zheng Tang, Shuo Wang, David C. Anastasiu +25

The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition fea…

cs.CV2024

The 8th AI City Challenge

Shuo Wang, David C. Anastasiu, Zheng Tang +21

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (I…

cs.CV2023

Scale-Aware Crowd Count Network with Annotation Error Correction

Yi-Kuan Hsieh, Jun-Wei Hsieh, Yu-Chee Tseng +2

Traditional crowd counting networks suffer from information loss when feature maps are downsized through pooling layers, leading to inaccuracies in counting crowds at a distance. E…

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