**Cycle-CenterNet** is a table structure parsing approach built on [CenterNet](https://paperswithcode.com/method/centernet) that uses a cycle-pairing module to simultaneously detect and group tabular cells into structured tables. It also utilizes a pairing loss which enables the grouping of discrete cells into the structured tables.

**CrossViT** is a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) that uses a dual-branch architecture to extract multi-scale feature representations for image classification. The architecture combines image patches (i.e. tokens in a [transformer](https://paperswithcode.com/method/transformer)) of different sizes to produce stronger visual features for image classification. It processes small and large patch tokens with two separate branches of different computational complexities and these tokens are fused together multiple times to complement each other.

Fusion is achieved by an efficient [cross-attention module](https://paperswithcode.com/method/cross-attention-module), in which each transformer branch creates a non-patch token as an agent to exchange information with the other branch by attention. This allows for linear-time generation of the attention map in fusion instead of quadratic time otherwise.

CrossViT

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

Cycle-CenterNet

Parsing Table Structures in the Wild

**GreedyNAS** is a one-shot [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. Previous methods held the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (eg, $7^{21}$). GreedyNAS eases the burden of supernet by encouraging focus more on evaluation of potentially-good candidates, which are identified using a surrogate portion of validation data. 

Concretely, during training, GreedyNAS utilizes a multi-path sampling strategy with rejection, and greedily filters the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. An exploration and exploitation policy is adopted by introducing an empirical candidate path pool.

Source	Parsing Table Structures in the Wild
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com