What is Adaptive Training Sample Selection (ATSS)?
Adaptive Training Sample Selection (ATSS) is a method that selects positive and negative samples by analyzing the statistical characteristics of an object. It combines the anchor-based and anchor-free detectors in computer vision to improve object detection models.
How does ATSS work?
ATSS selects positive samples by finding the candidate samples based on the center of the ground-truth box on each pyramid level. The number of candidate sampl
IoU-Balanced Sampling: A Method for Object Detection
If you've ever used a search engine to look for a specific image, you know how important it is to have accurate object detection for relevant results. But how do computer algorithms learn to recognize objects in images? One method is to use machine learning through deep neural networks, which requires large datasets of labeled images for training. However, not all training samples are equally useful, and some may even hinder the learning proc
Object detection datasets often include a large number of easy examples and only a few difficult ones, which can make training difficult. To address this issue, researchers have developed **OHEM**, or **Online Hard Example Mining**, which is a technique that improves the efficiency and effectiveness of training by automatically selecting difficult examples for training.
What is OHEM?
OHEM is a bootstrapping technique that modifies SGD, or Stochastic Gradient Descent, to selectively sample exa
PrIme Sample Attention (PISA): An Overview
Object detection is a crucial task in computer vision that involves identifying objects within an image or video stream. PrIme Sample Attention, or PISA, is a technique developed by researchers to improve the accuracy of object detection frameworks by training them to focus on prime samples. These prime samples are the most important for driving detection performance, making it essential to give them proper attention during the training process.
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