A **trainable 3D interaction space** aims to captures the associations between the triplet components and helps model the recognition of multiple triplets in the same frame.

Source: [Nwoye et al.](https://arxiv.org/pdf/2007.05405v1.pdf)

Image source: [Nwoye et al.](https://arxiv.org/pdf/2007.05405v1.pdf)

**AdaGPR** is an adaptive, layer-wise graph [convolution](https://paperswithcode.com/method/convolution) model. AdaGPR applies adaptive generalized Pageranks at each layer of a [GCNII](https://paperswithcode.com/method/gcnii) model by learning to predict the coefficients of generalized Pageranks using sparse solvers.

AdaGPR

Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank

3DIS

Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets

**MATE** is a [Transformer](https://paperswithcode.com/method/transformer) architecture designed to model the structure of web tables. It uses sparse attention in a way that allows heads to efficiently attend to either rows or columns in a table. Each attention head reorders the tokens by either column or row index and then applies a windowed attention mechanism. Unlike traditional self-attention, Mate scales linearly in the sequence length.

Source	Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com