May 14, 2019 · In this work, we propose an approach to increase the attention to local semantic segmentation performance by region-based hard region mining.
Apr 8, 2024 · We evaluate the proposed algorithm on PASCAL VOC 2012 and Cityscapes dataset, yielding a non-negligible improvement for semantic segmentation. 1 ...
OHEM, or Online Hard Example Mining, is a bootstrapping technique that modifies SGD to sample from examples in a non-uniform way depending on the current loss ...
Jan 27, 2021 · I recommend to checkout Online hard example mining (OHEM), region based mutual information loss (RMI) and hierarchical multiscale attention.
Semantic segmentation is considered as a per-pixel classification problem. Hard discriminate region existing in an image will limit segmentation accuracy. In ...
In this paper, we present the Stratified Online Hard Example Mining (S-OHEM) algorithm for training higher efficiency and accuracy detectors.
OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use that leads to consistent and significant ...
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Online hard example mining is based on the hypothesis that it is important to consider all RoIs in an image and then select hard examples for training. But what ...
We use a fusion of focal loss and regional mutual information loss instead of cross-entropy loss for the domain adaptation based semantic segmentation network.
Apr 2, 2021 · In this story, Training Region-based Object Detectors with Online Hard Example Mining, (OHEM), by Carnegie Mellon University, and Facebook AI Research (FAIR), ...