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Found 4 papers, 3 papers with code
Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models
Despite the remarkable success of deep learning in medical imaging analysis, medical image segmentation remains challenging due to the scarcity of high-quality labeled images for supervision.
Learning Latent Partial Matchings with Gumbel-IPF Networks
Learning to match discrete objects has been a central task in machine learning, often facilitated by a continuous relaxation of the matching structure.
Ranked #2 on
Graph Matching
on PASCAL VOC
Learning Constrained Structured Spaces with Application to Multi-Graph Matching
Multi-graph matching is a prominent structured prediction task, in which the predicted label is constrained to the space of cycle-consistent matchings.
Ranked #4 on
Graph Matching
on Willow Object Class
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization
In this work, we learn the variance of these randomized structured predictors and show that it balances better between the learned score function and the randomized noise in structured prediction.
