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Home/Authors/Chuan Zhang

Chuan Zhang

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

Vision×2Crypto×1

Frequent co-authors

Shaohui Dai1×
Yansong Qu1×
You Shen1×
Shengchuan Zhang1×
Liujuan Cao1×
Boyu Yuan1×

Research Timeline

2026
Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks

The paper proposes VerFU, a client-verifiable federated unlearning framework for low-altitude wireless networks that allows devices to ensure the server accurately removes their historical data contributions without revealing the original data.

GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

The paper proposes GloResNet, a lightweight 3D CNN that effectively predicts brain injury in preterm infants using T2-weighted MRI, achieving an average accuracy of 75.18%.

PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding

The paper introduces PAR3D, a unified part-aware 3D-MLLM framework, to enhance 3D scene understanding by enabling models to reason about and ground both whole objects and their fine-grained parts.

Highlighted terms show continued research focus across papers

Papers

cs.CVRecentJun 4, 2026

PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding

Shaohui Dai, Yansong Qu, You Shen, Shengchuan Zhang +1 more

The paper introduces PAR3D, a unified part-aware 3D-MLLM framework, to enhance 3D scene understanding by enabling models to reason about and ground both whole objects and their fine-grained parts.

View →
cs.CVRecentJun 1, 2026

GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

Boyu Yuan, Jiamiao Lu, Weichuan Zhang, Benqing Wu +4 more

The paper proposes GloResNet, a lightweight 3D CNN that effectively predicts brain injury in preterm infants using T2-weighted MRI, achieving an average accuracy of 75.18%.

View →
cs.CRRecentMar 31, 2026

Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks

Yuhua Xu, Mingtao Jiang, Chenfei Hu, Yinglong Wang +4 more

The paper proposes VerFU, a client-verifiable federated unlearning framework for low-altitude wireless networks that allows devices to ensure the server accurately removes their historical data contri…

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