**Self-supervised Equivariant Attention Mechanism**, or **SEAM**, is an attention mechanism for weakly supervised semantic segmentation. The SEAM applies consistency regularization on CAMs from various transformed images to provide self-supervision for network learning. To further improve the network prediction consistency, SEAM introduces the pixel correlation module (PCM), which captures context appearance information for each pixel and revises original CAMs by learned affinity attention maps. The SEAM is implemented by a [siamese network](https://paperswithcode.com/method/siamese-network) with equivariant cross regularization (ECR) loss, which regularizes the original CAMs and the revised CAMs on different branches.

The **Multiplex Molecular Graph Neural Network (MXMNet)** is an approach for the representation learning of molecules. The molecular interactions are divided into two categories: local and global. Then a two-layer multiplex graph $G = \\{ G_{l}, G_{g} \\}$ is constructed for a molecule. In $G$, the local layer $G_{l}$ only contains the local connections that mainly capture covalent interactions, and the global layer $G_{g}$ contains the global connections that cover non-covalent interactions. MXMNet uses the Multiplex Molecular (MXM) module that contains a novel angle-aware message passing operated on $G_{l}$ and an efficient message passing operated on $G_{g}$.

MXMNet

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

SEAM

Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

**LayoutLMv2** is an architecture and pre-training method for document understanding. The model is pre-trained with a great number of unlabeled scanned document images from the IIT-CDIP dataset, where some images in the text-image pairs are randomly replaced with another document image to make the model learn whether the image and OCR texts are correlated or not. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture, so that the model can fully understand the relative positional relationship among different text blocks.

Specifically, an enhanced Transformer architecture is used, i.e. a multi-modal Transformer asisthe backbone of LayoutLMv2. The multi-modal Transformer accepts inputs of three modalities: text, image, and layout. The input of each modality is converted to an embedding sequence and fused by the encoder. The model establishes deep interactions within and between modalities by leveraging the powerful Transformer layers.

Source	Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com