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Found 12 papers, 8 papers with code
GalLoP: Learning Global and Local Prompts for Vision-Language Models
Despite their success, most prompt learning methods trade-off between classification accuracy and robustness, e. g. in domain generalization or out-of-distribution (OOD) detection.
Energy Correction Model in the Feature Space for Out-of-Distribution Detection
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Optimization of Rank Losses for Image Retrieval
It provides an upperbound for rank losses and ensures robust training.
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs.
VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing
In this work, we introduce VidEdit, a novel method for zero-shot text-based video editing that guarantees robust temporal and spatial consistency.
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
HEAT complements prior density estimators of the ID density, e. g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation.
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 #2 on
Image Retrieval
on CUB-200-2011
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.
