**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.

**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.

TSRUs

Transformation-based Adversarial Video Prediction on Large-Scale Data

Dual Softmax Loss

Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

**CutMix** is an image data augmentation strategy. Instead of simply removing pixels as in [Cutout](https://paperswithcode.com/method/cutout), we replace the removed regions with a patch from another image. The ground truth labels are also mixed proportionally to the number of pixels of combined images. The added patches further enhance localization ability by requiring the model to identify the object from a partial view.

Source	Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com