Summary of Sood-imagenet: a Large-scale Dataset For Semantic Out-of-distribution Image Classification and Semantic Segmentation, by Alberto Bacchin et al.
SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation
by Alberto Bacchin, Davide Allegro, Stefano Ghidoni, Emanuele Menegatti
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 introduces SOOD-ImageNet, a novel dataset designed to assess the generalizability of computer vision models under out-of-distribution (OOD) conditions. The dataset consists of approximately 1.6 million images across 56 classes, focusing on semantic shift as a potential challenge. To address existing limitations in OOD benchmarks, SOOD-ImageNet leverages modern vision-language models and accurate human checks to ensure scalability and quality. Through extensive training and evaluation of various models, the paper showcases the potential of SOOD-ImageNet to advance OOD research in computer vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset called SOOD-ImageNet that helps test how well computer vision models work when they’re shown things they’ve never seen before. The dataset has millions of images and is designed to make sure the models can handle changes in what they’re looking at, like when an image shows something that’s not quite right. To make sure this dataset is good quality, the researchers used special tools that are really smart and had people check the images too. This will help other researchers make better computer vision models. |
