**Self-Supervised Temporal Domain Adaptation (SSTDA)** is a method for action segmentation with self-supervised temporal domain adaptation. It contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics.

**CBNet** is a backbone architecture that consists of multiple identical backbones (specially called Assistant Backbones and Lead Backbone) and composite connections between neighbor backbones. From left to right, the output of each stage in an Assistant Backbone, namely higher-level
features, flows to the parallel stage of the succeeding backbone as part of inputs through composite connections. Finally, the feature maps of the last backbone named Lead
Backbone are used for object detection. The features extracted by CBNet for object detection fuse the high-level and low-level features of multiple backbones, hence improve the detection performance.

CBNet

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

SSTDA

Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation

A **Deep Boltzmann Machine (DBM)** is a three-layer generative model. It is similar to a [Deep Belief Network](https://paperswithcode.com/method/deep-belief-network), but instead allows bidirectional connections in the bottom layers. Its energy function is  as an extension of the energy function of the RBM:

$$ E\left(v, h\right) = -\sum^{i}\_{i}v\_{i}b\_{i} - \sum^{N}\_{n=1}\sum_{k}h\_{n,k}b\_{n,k}-\sum\_{i, k}v\_{i}w\_{ik}h\_{k} - \sum^{N-1}\_{n=1}\sum\_{k,l}h\_{n,k}w\_{n, k, l}h\_{n+1, l}$$

for a DBM with $N$ hidden layers.

Source: [On the Origin of Deep Learning](https://arxiv.org/pdf/1702.07800.pdf)

Source	Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation
Year	2000
Data Source	Papers with Code archive (CC BY-SA)