**Conditional Relation Network**, or **CRN**, is a building block to construct more sophisticated structures for representation and reasoning over video. CRN takes as input an array of tensorial objects and a conditioning feature, and computes an array of encoded output objects. Model building becomes a simple exercise of replication, rearrangement and stacking of these reusable units for diverse modalities and contextual information. This design thus supports high-order relational and multi-step reasoning.

**Convolution-enhanced image Transformer** (**CeiT**) combines the advantages of CNNs in extracting low-level features, strengthening locality, and the advantages of Transformers in establishing long-range dependencies. Three modifications are made to the original Transformer: 1) instead of the straightforward tokenization from raw input images, we design an **Image-to-Tokens** (**I2T**) module that extracts patches from generated low-level features; 2) the feed-froward network in each encoder block is replaced with a **Locally-enhanced Feed-Forward** (**LeFF**) layer that promotes the correlation among neighbouring tokens in the spatial dimension; 3) a **Layer-wise Class token Attention** (**LCA**) is attached at the top of the Transformer that utilizes the multi-level representations.

CeiT

Incorporating Convolution Designs into Visual Transformers

Hierarchical Conditional Relation Networks for Video Question Answering

**DU-GAN** is a [generative adversarial network](https://www.paperswithcode.com/methods/category/generative-adversarial-networks) for LDCT denoising in medical imaging. The generator produces denoised LDCT images, and two independent branches with [U-Net](https://paperswithcode.com/method/u-net) based discriminators perform at the image and gradient domains. The U-Net based discriminator provides both global structure and local per-pixel feedback to the generator. Furthermore, the image discriminator encourages the generator to produce photo-realistic CT images while the gradient discriminator is utilized for better edge and alleviating streak artifacts caused by photon starvation.

Source	Hierarchical Conditional Relation Networks for Video Question Answering
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com