**TSRUs**, or **Transformation-based Spatial Recurrent Unit p**, is a modification of a [ConvGRU](https://paperswithcode.com/method/cgru) used in the [TriVD-GAN](https://paperswithcode.com/method/trivd-gan) architecture for video generation.

It largely follows [TSRUc](https://paperswithcode.com/method/tsruc), but computes each intermediate output in a fully sequential manner: like in TSRUc, $c$ is given access to $\hat{h}\_{t-1}$, but additionally, $u$ is given access to both outputs $\hat{h}\_{t-1}$ and $c$, so as to make an informed decision prior to mixing. This yields the following replacement for $u$:

$$ u = \sigma\left(W\_{u} \star\_{n}\left[\hat{h}\_{t-1};c\right] + b\_{u} \right) $$

In these equations $\sigma$ and $\rho$ are the elementwise sigmoid and [ReLU](https://paperswithcode.com/method/relu) functions respectively and the $\star\_{n}$ represents a [convolution](https://paperswithcode.com/method/convolution) with a kernel of size $n \times n$. Brackets are used to represent a feature concatenation.

**ShuffleNet V2 Block** is an image model block used in the [ShuffleNet V2](https://paperswithcode.com/method/shufflenet-v2) architecture, where speed is the metric optimized for (instead of indirect ones like FLOPs). It utilizes a simple operator called channel split. At the beginning of each unit, the input of $c$ feature channels are split into two branches with $c - c'$ and $c'$ channels, respectively. Following **G3**, one branch remains as identity. The other branch consists of three convolutions with the same input and output channels to satisfy **G1**. The two $1\times1$ convolutions are no longer group-wise, unlike the original [ShuffleNet](https://paperswithcode.com/method/shufflenet). This is partially to follow **G2**, and partially because the split operation already produces two groups. After [convolution](https://paperswithcode.com/method/convolution), the two branches are concatenated. So, the number of channels keeps the same (G1). The same “[channel shuffle](https://paperswithcode.com/method/channel-shuffle)” operation as in ShuffleNet is then used to enable information communication between the two branches.

The motivation behind channel split is that alternative architectures, where pointwise group convolutions and bottleneck structures are used, lead to increased memory access cost. Additionally more network fragmentation with group convolutions reduces parallelism (less friendly for GPU), and the element-wise addition operation, while they have low FLOPs, have high memory access cost. Channel split is an alternative where we can maintain a large number of equally wide channels (equally wide minimizes memory access cost) without having dense convolutions or too many groups.

ShuffleNet V2 Block

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

TSRUs

Transformation-based Adversarial Video Prediction on Large-Scale Data

**Dual Softmax Loss** is a loss function based on symmetric cross-entropy loss used in the [CAMoE](https://paperswithcode.com/method/camoe) video-text retrieval model. Every text and video are calculated the
similarity with other videos or texts, which should be maximum in terms of the ground truth pair. For DSL, a prior is introduced to revise the similarity score. Multiplying the prior with the original similarity matrix imposes an efficient constraint and can help to filter those single side match pairs. As a result, DSL highlights the one with both great Text-to-Video and Video-to-Text probability, conducting a more convincing result.

Source	Transformation-based Adversarial Video Prediction on Large-Scale Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com