The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...
Recent efforts have shown the importance of context on deep convolutional neural network based semantic segmentation. Among others, the predicted segmentation map (pmap) itself which encodes rich high ...
Semantic segmentation is critical in medical image processing, with traditional specialist models facing adaptation challenges to new tasks or distribution shifts. While both generalist pre-trained ...
Introduction: Weeds compete with crops for water, nutrients, and light, negatively impacting maize yield and quality. To enhance weed identification accuracy and meet the requirements of precision ...
1 Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA. 2 Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch, Tehran, ...
hi, thanks for your great work! I have another question regarding BEV semantic segmentation. Let's suppose that I want to train an e2e model using your Bench2Drive dataset, and need to utilize bev ...
Abstract: Semi-supervised learning offers an appealing solution for remote sensing (RS) image segmentation to relieve the burden of labor-intensive pixel-level labeling. However, RS images pose unique ...
Abstract: Bird-eye-view (BEV) perception for autonomous driving has become popular in recent years. Among various BEV perception tasks, moving-obstacle segmentation is very important, since it can ...
Thanks Google and UIUC researchers. A modified HRNet combined with semantic and instance multi-scale context achieves SOTA panoptic segmentation result on the Mapillary Vista challenge. See the paper.
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