Hugging Face Trending Papers
FSANet: Frequency-Spatial Aware Network for Image Segmentation
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arXiv Computer Vision
Sep 16
FSANet: Frequency-Spatial Aware Network for Image Segmentation
arXiv:2609.16773v1 Announce Type: new Abstract: Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accu...
arXiv Computer Vision
Sep 23
Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation
arXiv:2410.07421v2 Announce Type: replace Abstract: Instance segmentation is a core computer vision task with great practical significance. Recent advances, driven by large-scale benchmark datasets,...
arXiv AI
Sep 16
RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
arXiv:2508.19003v2 Announce Type: replace-cross Abstract: Roof plane segmentation is one of the key procedures for reconstructing three-dimensional (3D) building models at levels of detail (LoD) 2 an...
arXiv AI
Jun 16
Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation
arXiv:2606. 14912v1 Announce Type: cross Abstract: Despite great advances, finding accurate segmentation remains a challenging task, especially in scenarios with cluttered backgrounds, complex intensity variations and topology appearance.
Hugging Face Trending Papers
Sep 3
Feature Reconfiguration With Visual Prior for Medical Lesion Segmentation
Feature Reconfiguration With Visual Prior for Medical Lesion Segmentation proposes FreNet, a framework that reconfigures images and features before and during encoding to improve lesion segmentation. It introduces an Implicit Prior Neural Network that uses a visual prior from SAM to suppress background responses, and a Dual-domain Feature Reconfiguration module that decouples features in frequency and spatial domains to better handle diverse lesion morphology. Experiments on nine benchmarks across three imaging modalities show FreNet outperforms state‑of‑the‑art methods, achieving a 5.0% Dice improvement over the best prior method on the ETIS dataset.
arXiv AI
Aug 18
EA-LiteUNet: An Edge-Adaptive and Resource-Efficient U-Net for Boundary-Sensitive Dermoscopic Image Segmentation
arXiv:2608. 15537v1 Announce Type: cross Abstract: Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textures, and complex background artifacts.