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Haibin Ling

4 papers hereh-index 432 citations5 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.CV3
  • cs.LG1
same name
  • Haibin Ling — 10 papers, h 4
  • Haibin Ling — 8 papers, h 2
  • Haibin Ling — 4 papers, h 7
  • Haibin Ling — 3 papers, h 2
  • Haibin Ling — 2 papers, h 2
  • Haibin Ling — 2 papers, h 8

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.CV2026

Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting

Wen Zhang, Qin Ren, Wenjing Liu +2

Accurate nuclear instance segmentation is a pivotal task in computational pathology, supporting data-driven clinical insights and facilitating downstream translational applications…

cs.CV2025

Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection

Jifeng Shen, Haibo Zhan, Shaohua Dong +3

Modern multispectral feature fusion for object detection faces two critical limitations: (1) Excessive preference for local complementary features over cross-modal shared semantics…

cs.CV2025

OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport

Qin Ren, Yifan Wang, Ruogu Fang +2

Survival prediction using whole slide images (WSIs) can be formulated as a multiple instance learning (MIL) problem. However, existing MIL methods often fail to explicitly capture…

cs.LG2025

RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation

Jingxiang Qu, Wenhan Gao, Jiaxing Zhang +4

3D Geometric Graph Neural Networks (GNNs) have emerged as transformative tools for modeling molecular data. Despite their predictive power, these models often suffer from limited i…

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