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Found 6 papers, 4 papers with code
Memory transformers for full context and high-resolution 3D Medical Segmentation
Transformer models achieve state-of-the-art results for image segmentation.
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction
Since BC is an approximate physical model violated in several situations, we propose to train a physically-constrained network complemented with a data-driven network.
Hierarchical Average Precision Training for Pertinent Image Retrieval
Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity.
Ranked #1 on
Metric Learning
on DyML-Animal
Robust and Decomposable Average Precision for Image Retrieval
In image retrieval, standard evaluation metrics rely on score ranking, e. g. average precision (AP).
Ranked #1 on
Image Retrieval
on SOP
Urban Surface Reconstruction in SAR Tomography by Graph-Cuts
This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces.
U-Net Transformer: Self and Cross Attention for Medical Image Segmentation
Medical image segmentation remains particularly challenging for complex and low-contrast anatomical structures.
