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Xinran Li

Dalian University of Technonoly

4 papers hereh-index 439 citations11 works total

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

author position
  • first author3
  • middle author1

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

fields
  • cs.CV2
  • cs.AI1
  • cs.LG1
affiliations
  • Dalian University of Technonoly
Homepage
same name
  • Xinran Li — 17 papers, h 14
  • Xinran Li — 8 papers, h 6
  • Xinran Li — 7 papers, h 4
  • Xinran Li — 3 papers, h 4
  • Xinran Li — 2 papers
  • Xinran Li — 2 papers

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

collaborators

4 papers

cs.AI2025

Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning

Xinran Li, Yu Liu, Jiaqi Qiao +1

Emotion Recognition in Conversation (ERC) is a crucial task for understanding human emotions and enabling natural human-computer interaction. Although Large Language Models (LLMs)…

cs.LG2025

Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

Xinran Li, Xiujuan Xu, Jiaqi Qiao

Emotion Recognition in Conversation (ERC) is a practical and challenging task. This paper proposes a novel multimodal approach, the Long-Short Distance Graph Neural Network (LSDGNN…

cs.CV2025

CheX-DS: Improving Chest X-ray Image Classification with Ensemble Learning Based on DenseNet and Swin Transformer

Xinran Li, Yu Liu, Xiujuan Xu +1

The automatic diagnosis of chest diseases is a popular and challenging task. Most current methods are based on convolutional neural networks (CNNs), which focus on local features w…

cs.CV2024

Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations

Cheng Lei, Jie Fan, Xinran Li +4

Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly…

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