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Found 8 papers, 2 papers with code
Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data
Once trained, SHMGAN is able to generate specular-free images from a single RGB image as input; without requiring any additional external labels.
SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images
Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest.
Physically-admissible polarimetric data augmentation for road-scene analysis
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis.
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification
(ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance.
Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters
However, the effectiveness of these approaches heavily depends on the choice of some hyperparameters (HP) that affect the generation of pseudo-labels by clustering.
Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling
A challenge for re-ID is the performance preservation when a model is used on data of interest (target data) which belong to a different domain from the training data domain (source data).
Direct estimation of fetal head circumference from ultrasound images based on regression CNN
In this paper, we depart from this idea and propose to leverage the ability of convolutional neural networks (CNN) to directly measure the head circumference, without having to resort to handcrafted features or manually labeled segmented images.
Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning
The efficiency of the proposed method is mostly due to the high power of the polarimetry to discriminate any object by its reflective properties and on the use of deep neural networks for object detection.
