Search Results for author:
Found 5 papers, 3 papers with code
Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and-rescue.
Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty.
High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation
Our work is based on two techniques for improving CAMs; importance sampling, which is a substitute for GAP, and the feature similarity loss, which utilizes a heuristic that object contours almost always align with color edges in images.
Balanced Product of Calibrated Experts for Long-Tailed Recognition
We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error.
Long-tail Learning
Long-tail Learning on CIFAR-10-LT (ρ=100)
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Importance Sampling CAMs for Weakly-Supervised Segmentation
Classification networks can be used to localize and segment objects in images by means of class activation maps (CAMs).
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
