**PnP**, or **Poll and Pool**, is sampling module extension for [DETR](https://paperswithcode.com/method/detr)-type architectures that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The [transformer](https://paperswithcode.com/method/transformer) models information interaction within the fine-coarse feature space and translates the features into the detection result.

SCAN automatically groups images into semantically meaningful clusters when ground-truth annotations are absent. SCAN is a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task is employed to obtain semantically meaningful features. Second, the obtained features are used as a prior in a learnable clustering  approach.

Image source: [Gansbeke et al.](https://arxiv.org/pdf/2005.12320v2.pdf)

SCAN-clustering

SCAN: Learning to Classify Images without Labels

PnP-DETR: Towards Efficient Visual Analysis with Transformers

Recent deep neural network models trained on smaller and less diverse datasets use data augmentation to alleviate limitations such as overfitting, reduced robustness, and lower generalization. Methods using 3D datasets are among the most common to use data augmentation techniques such as random point drop, scaling, translation, rotations, and jittering. However, these data augmentation techniques are fixed and are often applied to the entire object, ignoring the object’s local geometry. Different local neighborhoods on the object surface hold a different amount of geometric complexity. Applying the same data augmentation techniques at the object level is less effective in augmenting local neighborhoods with complex structures. This paper presents PatchAugment, a data augmentation framework to apply different augmentation techniques to the local neighborhoods. Our experimental studies on PointNet++ and DGCNN models demonstrate the effectiveness of PatchAugment on the task of 3D Point Cloud Classification. We evaluated our technique against these models using four benchmark datasets, ModelNet40 (synthetic), ModelNet10 (synthetic), SHREC’16 (synthetic), and ScanObjectNN (real-world).

[[ICCVW 2021]](https://openaccess.thecvf.com/content/ICCV2021W/DLGC/papers/Sheshappanavar_PatchAugment_Local_Neighborhood_Augmentation_in_Point_Cloud_Classification_ICCVW_2021_paper.pdf) PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification. [[Code]](https://github.com/VimsLab/PatchAugment)

Source	PnP-DETR: Towards Efficient Visual Analysis with Transformers
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com