18 citations · 31 across the 16 of their papers we have counts for
4 papers · 1 filter
Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation
Loic Themyr, Clement Rambour, Nicolas Thome +2
Transformers have proved to be very effective for visual recognition tasks. In particular, vision transformers construct compressed global representations through self-attention an…
Memory transformers for full context and high-resolution 3D Medical Segmentation
Loic Themyr, Clément Rambour, Nicolas Thome +2
Transformer models achieve state-of-the-art results for image segmentation. However, achieving long-range attention, necessary to capture global context, with high-resolution 3D im…
Hierarchical Average Precision Training for Pertinent Image Retrieval
Elias Ramzi, Nicolas Audebert, Nicolas Thome +2
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. This…
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction
Vincent Le Guen, Clément Rambour, Nicolas Thome
State-of-the-art methods for optical flow estimation rely on deep learning, which require complex sequential training schemes to reach optimal performances on real-world data. In t…