**Position-Sensitive RoI Pooling layer** aggregates the outputs of the last convolutional layer and generates scores for each RoI. Unlike [RoI Pooling](https://paperswithcode.com/method/roi-pooling), PS RoI Pooling conducts selective pooling, and each of the $k$ × $k$ bin aggregates responses from only one score map out of the bank of $k$ × $k$ score maps. With end-to-end training, this RoI layer shepherds the last convolutional layer to learn specialized position-sensitive score maps.

**Self-adaptive Training** is a training algorithm that dynamically corrects problematic training labels by model predictions to improve generalization of deep learning for potentially corrupted training data. Accumulated predictions are used to augment the training dynamics. The use of an exponential-moving-average scheme alleviates the instability issue of model predictions, smooths out the training target during the training process and enables the algorithm to completely change the training labels if necessary.

Self-adaptive Training

Self-Adaptive Training: beyond Empirical Risk Minimization

Position-Sensitive RoI Pooling

R-FCN: Object Detection via Region-based Fully Convolutional Networks

The **Vision Transformer**, or **ViT**, is a model for image classification that employs a [Transformer](https://paperswithcode.com/method/transformer)-like architecture over patches of the image.  An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard [Transformer](https://paperswithcode.com/method/transformer) encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.

Source	R-FCN: Object Detection via Region-based Fully Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com