# Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding

arXiv: https://arxiv.org/abs/2210.17417 · HTML: https://mlsanity.com/paper/2210.17417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Exploring Models and Data for Image Question Answering](https://mlsanity.com/paper/1505.02074/) (2015)
- [Automatic Spatially-aware Fashion Concept Discovery](https://mlsanity.com/paper/1708.01311/) (2017)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [Word Representations via Gaussian Embedding](https://mlsanity.com/paper/1412.6623/) (2014)
- [DeepStyle: Multimodal Search Engine for Fashion and Interior Design](https://mlsanity.com/paper/1801.03002/) (2018)
- [Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning](https://mlsanity.com/paper/2104.02429/) (2021)
- [Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2108.04527/) (2021)
- [Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty](https://mlsanity.com/paper/2006.10932/) (2020)
- [α-Geodesical Skew Divergence](https://mlsanity.com/paper/2103.17060/) (2021)
- [NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation](https://mlsanity.com/paper/2202.05456/) (2022)
- [Information Geometry of Dropout Training](https://mlsanity.com/paper/2206.10936/) (2022)

## Built on by (most cited first, 1 of 1)

- [Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning](https://mlsanity.com/paper/2211.06688/) (2022)

## Abstract (arXiv)

Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded representation of target entity as a point in the projective space. Some models, such as Word2Gauss, assume a probability distribution behind the embedded representation, which enables the spread or variance of the meaning of embedded target components to be captured and considered in more detail. We examine the method of estimating embedded representations as probability distributions for the interpretation of fashion-specific abstract and difficult-to-understand terms. Terms, such as "casual," "adult-casual,'' "beauty-casual," and "formal," are extremely subjective and abstract and are difficult for both experts and non-experts to understand, which discourages users from trying new fashion. We propose an end-to-end model called dual Gaussian visual-semantic embedding, which maps images and attributes in the same projective space and enables the interpretation of the meaning of these terms by its broad applications. We demonstrate the effectiveness of the proposed method through multifaceted experiments involving image and attribute mapping, image retrieval and re-ordering techniques, and a detailed theoretical/analytical discussion of the distance measure included in the loss function.