Learning Type-A… (2018-03-25T00:00:00.000000Z)

TL;DR

This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model.

Abstract

Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3–5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries (Code and data: https://github.com/mvasil/fashion-compatibility).

Authors

D. Forsyth

2 papers

Bryan A. Plummer

6 papers

Mariya I. Vasileva

1 papers

References44 items

Be Your Own Prada: Fashion Synthesis with Structural Coherence

Dress Like a Star: Retrieving Fashion Products from Videos

Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

Multi-modal joint embedding for fashion product retrieval

Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

TL;DR

This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model.

Abstract

Authors

D. Forsyth

2 papers

Bryan A. Plummer

6 papers

Mariya I. Vasileva

1 papers

TL;DR

Abstract

Authors

References44 items

Be Your Own Prada: Fashion Synthesis with Structural Coherence

Dress Like a Star: Retrieving Fashion Products from Videos

Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

Multi-modal joint embedding for fashion product retrieval

Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

TL;DR

Abstract

Authors

References44 items

Be Your Own Prada: Fashion Synthesis with Structural Coherence

Dress Like a Star: Retrieving Fashion Products from Videos

Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

Multi-modal joint embedding for fashion product retrieval

Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

Learning Unified Embedding for Apparel Recognition

Learning Fashion Compatibility with Bidirectional LSTMs

Learning the Latent “Look”: Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images

Multi-modal Embedding for Main Product Detection in Fashion

Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search

Sampling Matters in Deep Embedding Learning

Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces

Fashion Forward: Forecasting Visual Style in Fashion

Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images

The Elements of Fashion Style

Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data

End-to-End Localization and Ranking for Relative Attributes

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction

SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions

Learning Compatibility Across Categories for Heterogeneous Item Recommendation

Conditional Similarity Networks

Fast Training of Triplet-Based Deep Binary Embedding Networks

Deep Residual Learning for Image Recognition

Where to Buy It: Matching Street Clothing Photos in Online Shops

Just Noticeable Differences in Visual Attributes

Rethinking the Inception Architecture for Computer Vision

Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences

Learning visual similarity for product design with convolutional neural networks

Neuroaesthetics in fashion: Modeling the perception of fashionability

Deep domain adaptation for describing people based on fine-grained clothing attributes

FaceNet: A unified embedding for face recognition and clustering

Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation

Hipster Wars: Discovering Elements of Fashion Styles

Fine-Grained Visual Comparisons with Local Learning

Style Finder: Fine-Grained Clothing Style Detection and Retrieval

Efficient Estimation of Word Representations in Vector Space

Hi, magic closet, tell me what to wear!

Describing Clothing by Semantic Attributes

ImageNet: A large-scale hierarchical image database

Learning a Distance Metric from Relative Comparisons

Signature Verification Using A "Siamese" Time Delay Neural Network

Mix and Match: Joint Model for Clothing and Attribute Recognition

Visualizing Data using t-SNE

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