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Jingjing Chen

60 papers hereh-index 376k citations167 works total

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

author position
  • middle author46
  • last author10

Across the 56 of 60 papers where every author was matched, so the position is known.

fields
  • cs.CV47
  • cs.AI4
  • cs.RO3
  • cs.CL2
  • cs.MM2
  • cs.CR1
same name
  • Jingjing Chen — 12 papers, h 7
  • Jingjing Chen — 2 papers, h 12
  • Jingjing Chen — 2 papers, h 5
  • Jingjing Chen — 2 papers, h 5
  • Jingjing Chen — 2 papers, h 11
  • Jingjing Chen — 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

works on
catastrophic forgetting 1continual learning 1decision boundary drift 1generative model detection 1image forensics 1

From the 1 of 60 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

ReGraph: Learning to Generate Recipe Graphs from Food Images

Guoshan Liu, Bin Zhu, Pengkun Jiao +3

Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.However, cooking is a structured transformation process in which in…

cs.AI2026

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Xingjun Ma, Yixu Wang, Hengyuan Xu +18

The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and…

cs.AI2025

Efficient Test-Time Retrieval Augmented Generation

Hailong Yin, Bin Zhu, Jingjing Chen +1

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG)…

cs.AI2025

Don't Deceive Me: Mitigating Gaslighting through Attention Reallocation in LMMs

Pengkun Jiao, Bin Zhu, Jingjing Chen +2

Large Multimodal Models (LMMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their vulnerability to user gaslighting-the deliberate use of mislea…

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