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Zhong Ji

5 papers hereh-index 10324 citations29 works total

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

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG1
same name
  • Zhong Ji — 26 papers, h 30
  • Zhong Ji — 8 papers, h 6
  • Zhong Ji — 2 papers, h 1
  • Zhong Ji — 2 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
20222026
most citedAsymmetric Cross-Scale Alignment for Text-Based Person Search

2 citations · 2 across the 3 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Calibrate Before Reason: Robust Visual Token Reduction against Semantic Drift in VLMs

Jiasheng Li, Zhong Ji, Yan Zhang +1

Large Vision-Language Models (VLMs) suffer from prohibitive inference overhead due to long sequences of visual tokens. However, existing visual token reduction methods mainly impro…

cs.CV2026

TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering

Zhong Ji, Keqi Jin, Yan Zhang +1

Long-document multimodal question answering requires more than retrieving relevant chunks from a large document. Different queries require different evidence behavior. Existing mul…

cs.CV2025

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning

Zhong Ji, Weilong Cao, Yan Zhang +3

Diffusion models has emerged as a powerful framework for tasks like image controllable generation and dense prediction. However, existing models often struggle to capture underlyin…

cs.CV2022★ 2 cited

Asymmetric Cross-Scale Alignment for Text-Based Person Search

Zhong Ji, Junhua Hu, Deyin Liu +2

Text-based person search (TBPS) is of significant importance in intelligent surveillance, which aims to retrieve pedestrian images with high semantic relevance to a given text desc…

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