Related papers: MMFashion: An Open-Source Toolbox for Visual Fashi…

Multimodal LLMs (MLLMs) are capable of performing complex data analysis, visual question answering, generation, and reasoning tasks. However, their ability to analyze biometric data is relatively underexplored. In this work, we investigate…

Automatic group emotion recognition plays an important role in understanding complex human-human interaction. This paper introduces, Emolysis, a Python-based, standalone open-source group emotion analysis toolkit for use in different social…

Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness…

Fashion image generation has so far focused on narrow tasks such as virtual try-on, where garments appear in clean studio environments. In contrast, editorial fashion presents garments through dynamic poses, diverse locations, and carefully…

Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is…

Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of…

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each…

The paper presents a novel concept that analyzes and visualizes worldwide fashion trends. Our goal is to reveal cutting-edge fashion trends without displaying an ordinary fashion style. To achieve the fashion-based analysis, we created a…

Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised in-the-wild methods, which improve adaptability but remain…

Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and…

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…

In this study, we propose a technology called the Fashion Intelligence System based on the visual-semantic embedding (VSE) model to quantify abstract and complex expressions unique to fashion, such as ''casual,'' ''adult-casual,'' and…

This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising of many checks related to various types of issues, such as model…

TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In…

Fashion is a fast-changing industry where designs are refreshed at large scale every season. Moreover, it faces huge challenge of unsold inventory as not all designs appeal to customers. This puts designers under significant pressure.…

Learning an effective outfit-level representation is critical for predicting the compatibility of items in an outfit, and retrieving complementary items for a partial outfit. We present a framework, OutfitTransformer, that uses the proposed…

When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods…

While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification tasks, which rely heavily on deep learning. To bridge this…

Fashion is an important part of human experience. Events such as interviews, meetings, marriages, etc. are often based on clothing styles. The rise in the fashion industry and its effect on social influencing have made outfit compatibility…

Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fashion domain, existing V+L methods are inadequate as they…