9 citations · 11 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection
Hu Zhang, Jianhua Xu, Tao Tang +4
Traditional LiDAR-based object detection research primarily focuses on closed-set scenarios, which falls short in complex real-world applications. Directly transferring existing 2D…
Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang, Xin Shen, Heming Du +10
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images ar…
BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge
Chen Liu, Peike Li, Hu Zhang +4
Given an audio-visual pair, audio-visual segmentation (AVS) aims to locate sounding sources by predicting pixel-wise maps. Previous methods assume that each sound component in an a…