9 citations · 10 across the 10 of their papers we have counts for
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Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval
Shijie Wang, Yadan Luo, Zijian Wang +2
Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen categories. However, such supervis…
Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation
Shijie Wang, Zijian Wang, Yadan Luo +3
Wheat disease segmentation is fundamental to precision agriculture but faces severe challenges from significant intra-class temporal variations across growth stages. Such substanti…
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…
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…
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…
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…
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…
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…
Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild
Tianqi Wei, Zhi Chen, Xin Yu
Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on…
Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline
Tianqi Wei, Zhi Chen, Zi Huang +1
Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degra…