# FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation

Source: [FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation](<https://www.sota2.com/research/paper/68fa74ca1e209131cea63dac>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:40:00.946Z

Authors: Riza Velioglu, Robin Chan, Barbara Hammer

Published: 2024-04-12T16:28:30.000Z

## Abstract

In the realm of fashion object detection and segmentation for online shopping images, existing state-of-the-art fashion parsing models encounter limitations, particularly when exposed to non-model-worn apparel and close-up shots. To address these failures, we introduce FashionFail; a new fashion dataset with e-commerce images for object detection and segmentation. The dataset is efficiently curated using our novel annotation tool that leverages recent foundation models. The primary objective of FashionFail is to serve as a test bed for evaluating the robustness of models. Our analysis reveals the shortcomings of leading models, such as Attribute-Mask R-CNN and Fashionformer. Additionally, we propose a baseline approach using naive data augmentation to mitigate common failure cases and improve model robustness. Through this work, we aim to inspire and support further research in fashion item detection and segmentation for industrial applications. The dataset, annotation tool, code, and models are available at \\url{https://rizavelioglu.github.io/fashionfail/}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.08582>)

[PDF](<https://arxiv.org/pdf/2404.08582.pdf>)