5 papers
AutoMine Solution for AV2 2026 Scenario Mining Challenge
Songliang Cao, Jiele Zhao, Yuru Wang +10
With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs has become essential for data-…
Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species
Jinyu Xu, Tianqi Hu, Xiaonan Hu +4
Visually cataloging and quantifying the natural world requires pushing the boundaries of both detailed visual classification and counting at scale. Despite significant progress, pa…
DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data
Jiafei Zhang, Songliang Cao, Binghui Xu +6
DepthCropSeg++: a foundation model for crop segmentation, capable of segmenting different crop species under open in-field environment. Crop segmentation is a fundamental task for…
FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Bing Han, Chen Zhu, Dong Han +22
Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the intera…
First Place Solution to the MLCAS 2025 GWFSS Challenge: The Devil is in the Detail and Minority
Songliang Cao, Tianqi Hu, Hao Lu
In this report, we present our solution during the participation of the MLCAS 2025 GWFSS Challenge. This challenge hosts a semantic segmentation competition specific to wheat plant…