In contrast to typical GANs, a U-Net GAN uses a segmentation network as the discriminator. This segmentation network predicts two classes: real and fake. In doing so, the discriminator gives the generator region-specific feedback. This discriminator design also enables a  [CutMix](https://paperswithcode.com/method/cutmix)-based consistency regularization on the two-dimensional output of the U-Net GAN discriminator, which further improves image synthesis quality.

**MDETR** is an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. It utilizes a [transformer](https://paperswithcode.com/method/transformer)-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. The network is pre-trained on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. The network is then fine-tuned on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation.

MDETR

MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding

U-Net GAN

A U-Net Based Discriminator for Generative Adversarial Networks

**LeVIT** is a hybrid neural network for fast inference image classification. LeViT is a stack of [transformer blocks](https://paperswithcode.com/method/transformer), with [pooling steps](https://paperswithcode.com/methods/category/pooling-operation) to reduce the resolution of the activation maps as in classical [convolutional architectures](https://paperswithcode.com/methods/category/convolutional-neural-networks). This replaces the uniform structure of a Transformer by a pyramid with pooling, similar to the [LeNet](https://paperswithcode.com/method/lenet) architecture

Source	A U-Net Based Discriminator for Generative Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com