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Zehang Lin

3 accepted papers

2026

Conflict-Aware Client Selection for Multi-Server Federated Learning

ICASSP 2026poster

Federated learning (FL) has emerged as a promising distributed machine learning (ML) that enables collaborative model training across clients without exposing raw data, thereby preserving user privacy and reducing communication costs. Despite these benefits, traditional single-server FL suffers from…

Cited by 0SourcePDFScholar
2026

Subject-Agnostic Cross-View Referring Expression Comprehension via Reward-Driven Consistency Learning

IJCAI 2026

Cross-view Referring Expression Comprehension (REC) requires a model to accurately localize objects in a spatial representation of one perspective based on descriptions generated from a different perspective. Essentially, it demands maintaining referential consistency under perspective shifts. This

Cited by 0Scholar
2021

DepthGrasp: Depth Completion of Transparent Objects Using Self-Attentive Adversarial Network with Spectral Residual for Grasping

IROS 2021poster

Transparent objects with unique visual properties often make depth cameras fail to scan their reflective and refractive surfaces. Recent studies on depth completion of transparent objects have leveraged a linear system based on the geometric constraints to predict the missing depth, which is hard to…

Cited by 45SourceScholar