**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.

**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.

Dual Softmax Loss

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

CutMix

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

**Field Embedded Factorization Machine**, or **FEFM**, is a factorization machine variant. For each field pair, FEFM introduces symmetric matrix embeddings along with the usual feature vector embeddings that are present in FM. Like FM, $v\_{i}$ is the vector embedding of the $i^{t h}$ feature. However, unlike Field-Aware Factorization Machines (FFMs), FEFM doesn't explicitly learn field-specific feature embeddings. The learnable symmetric matrix $W\_{F(i), F(j)}$ is the embedding for the field pair $F(i)$ and $F(j) .$ The interaction between the $i^{t h}$ feature and the $j^{t h}$ feature is mediated through $W_{F(i), F(j)} .$

$$
\phi(\theta, x)=\phi\_{F E F M}((w, v, W), x)=w\_{0}+\sum\_{i=1}^{m} w_{i} x_{i}+\sum\_{i=1}^{m} \sum\_{j=i+1}^{m} v\_{i}^{T} W\_{F(i), F(j)} v\_{j} x\_{i} x\_{j}
$$

where $W\_{F(i), F(j)}$ is a $k \times k$ symmetric matrix ( $k$ is the dimension of the feature vector embedding space containing feature vectors $v\_{i}$ and $v\_{j}$ ).

The symmetric property of the learnable matrix $W\_{F(i), F(j)}$ is ensured by reparameterizing $W\_{F(i), F(j)}$ as $U\_{F(i), F(j)}+$ $U\_{F(i), F(j)}^{T}$, where $U\_{F(i), F(j)}^{T}$ is the transpose of the learnable matrix $U\_{F(i), F(j)} .$ Note that $W_{F(i), F(j)}$ can also be interpreted as a vector transformation matrix which transforms a feature embedding when interacting with a specific field.

Source	CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com