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20242026
most citedPlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation

9 citations · 9 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components

Zhongtian Huang, Zhi Chen, Zi Huang +8

Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agricultu…

cs.CV2025

Distributed Zero-Shot Learning for Visual Recognition

Zhi Chen, Yadan Luo, Zi Huang +3

In this paper, we propose a Distributed Zero-Shot Learning (DistZSL) framework that can fully exploit decentralized data to learn an effective model for unseen classes. Considering…

cs.CV2025

Cluster-Aware Prompt Ensemble Learning for Few-Shot Vision-Language Model Adaptation

Zhi Chen, Xin Yu, Xiaohui Tao +2

Vision-language models (VLMs) such as CLIP achieve zero-shot transfer across various tasks by pre-training on numerous image-text pairs. These models often benefit from using an en…

cs.CV2025

Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation

Tianqi Wei, Xin Yu, Zhi Chen +2

Accurate segmentation of foliar diseases and insect damage in wheat is crucial for effective crop management and disease control. However, the insect damage typically occupies only…

cs.CV2024

CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction

Zhi Chen, Tianqi Wei, Zecheng Zhao +6

In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge…

cs.CV20249 cited

PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation

Tianqi Wei, Zhi Chen, Xin Yu +3

Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image…