**GreedyNAS-C** is a convolutional neural network discovered using the [GreedyNAS](https://paperswithcode.com/method/greedynas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building blocks used are inverted residual blocks (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)) and squeeze-and-excitation blocks.

The **Local Relation Network** (**LR-Net**) is a network built with local relation layers which represent a feature image extractor. This feature extractor adaptively determines aggregation weights based on the compositional relationship of local pixel pairs.

LRNet

Local Relation Networks for Image Recognition

GreedyNAS-C

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

Florence is a computer vision foundation model aiming to learn universal visual-language representations that be adapted to various computer vision tasks, visual question answering, image captioning, video retrieval, among other tasks. Florence's workflow consists of data curation, unified learning, Transformer architectures and adaption. Florence is pre-trained in an image-label-description space, utilizing a unified image-text contrastive learning. It involves a two-tower architecture: 12-layer Transformer for the language encoder, and a Vision Transformer for the image encoder. Two linear projection layers are added on top of the image encoder and language encoder to match the dimensions of image and language features. Compared to previous methods for cross-modal shared representations, Florence expands beyond simple classification and retrieval capabilities to advanced representations that support object level, multiple modality, and videos respectively.

Source	GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: GreedyNAS-C?

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