Visual Parsing is a vision and language pretrained model that adopts self-attention for visual feature learning where each visual token is an approximate weighted mixture of all tokens. Thus, visual parsing provides the dependencies of each visual token pair.  It helps better learning of visual relation with the language and promote inter modal alignment. The model is composed of a vision Transformer that takes an image as input and outputs the visual tokens and a multimodal Transformer. 
It applies a linear layer and a Layer Normalization to embed the vision tokens. It follows BERT to get word embeddings. Vision and language tokens are concatenated to form the input sequences. A multi-modal Transformer is used to fuse the vision and language modality. A metric named Inter-Modality Flow (IMF) is used to quantify the interactions between two modalities.
Three pretraining tasks are adopted: Masked Language Modeling (MLM), Image-Text Matching (ITM), and Masked Feature Regression (MFR). MFR is a novel task that is included to mask visual tokens with similar or correlated semantics in this framework.

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data.

Set Transformer

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Visual Parsing

Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training

**Batchboost** is a variation on [MixUp](https://paperswithcode.com/method/mixup) that instead of mixing just two images, mixes many images together.

Source	Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com