Summary of Fashionfail: Addressing Failure Cases in Fashion Object Detection and Segmentation, by Riza Velioglu et al.
FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation
by Riza Velioglu, Robin Chan, Barbara Hammer
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of existing fashion parsing models in detecting and segmenting objects in online shopping images. The authors introduce FashionFail, a new dataset with e-commerce images for object detection and segmentation, which is curated using a novel annotation tool leveraging recent foundation models. The primary goal of FashionFail is to evaluate the robustness of models, revealing shortcomings of leading models like Attribute-Mask R-CNN and Fashionformer. To mitigate common failure cases, the authors propose a baseline approach using naive data augmentation. This work aims to inspire further research in fashion item detection and segmentation for industrial applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fashion experts are working on computers to make it easier to find objects in pictures from online stores. Right now, these computer models can’t always recognize things correctly. The problem is that they were trained on pictures of people wearing clothes, not just any old object. To help fix this issue, the researchers created a new set of pictures called FashionFail. This set includes lots of different types of objects and close-up shots. They wanted to see which computer models could work well with these kinds of images. The results showed that most models don’t do very well, especially when things get tricky like with close-up shots. To make the models better, the researchers came up with a simple way to use extra pictures to help train them. This will hopefully lead to better fashion detection and segmentation in online stores. |
Keywords
» Artificial intelligence » Cnn » Data augmentation » Mask » Object detection » Parsing
