**CSPDenseNet** is a convolutional neural network and object detection backbone where we apply the Cross Stage Partial Network (CSPNet) approach to [DenseNet](https://paperswithcode.com/method/densenet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

**Slot Attention** is an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. Using an iterative attention mechanism, slots produces a set of output vectors with permutation symmetry. Unlike capsules used in Capsule Networks, slots produced by Slot Attention do not specialize to one particular type or class of object, which could harm generalization. Instead, they act akin to object files, i.e., slots use a common representational format: each slot can store (and bind to) any object in the input. This allows Slot Attention to generalize in a systematic way to unseen compositions, more objects, and more slots.

Slot Attention

Object-Centric Learning with Slot Attention

CSPDenseNet

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

**DFDNet**, or **DFDNet**, is a deep face dictionary network for face restoration to guide the restoration process of degraded observations. Given a LQ image $I\_{d}$, the DFDNet selects the dictionary features that have the most similar structure with the input. Specially, we re-norm the whole dictionaries via component AdaIN (termed as CAdaIN) based on the input component to eliminate the distribution or style diversity. The selected dictionary features are then utilized to guide the restoration process via dictionary feature transformation.

Source	CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com