A **3D Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) where the kernel slides in 3 dimensions as opposed to 2 dimensions with 2D convolutions. One example use case is medical imaging where a model is constructed using 3D image slices. Additionally video based data has an additional temporal dimension over images making it suitable for this module. 

Image: Lung nodule detection based on 3D convolutional neural networks, Fan et al

The multi-head of mixed attention combines both self- and cross-attentions, encouraging high-level learning of interactions between entities captured in the various attention features. It is build with several attention heads, each of the head can implement either self or cross attention. A self attention is when the key and query features are the same or come from the same domain features. A cross attention is when the key and query features are generated from different features. Modeling MHMA allows a model to identity the relationship between features of different domains. This is very useful in tasks involving relationship modeling such as human-object interaction, tool-tissue interaction, man-machine interaction, human-computer interface, etc.

MHMA

Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos

3D Convolution

**LAMB** is a a layerwise adaptive large batch optimization technique. It provides a strategy for adapting the learning rate in large batch settings. LAMB uses [Adam](https://paperswithcode.com/method/adam) as the base algorithm and then forms an update as:

$$r\_{t} = \frac{m\_{t}}{\sqrt{v\_{t}} + \epsilon}$$
$$x\_{t+1}^{\left(i\right)} = x\_{t}^{\left(i\right)}  - \eta\_{t}\frac{\phi\left(|| x\_{t}^{\left(i\right)} ||\right)}{|| m\_{t}^{\left(i\right)} || }\left(r\_{t}^{\left(i\right)}+\lambda{x\_{t}^{\left(i\right)}}\right) $$

Unlike [LARS](https://paperswithcode.com/method/lars), the adaptivity of LAMB is two-fold: (i) per dimension normalization with respect to the square root of the second moment used in Adam and (ii) layerwise normalization obtained due to layerwise adaptivity.

Year	2015
Data Source	CC BY-SA - https://paperswithcode.com