Summary of Crisisvit: a Robust Vision Transformer For Crisis Image Classification, by Zijun Long and Richard Mccreadie and Muhammad Imran
CrisisViT: A Robust Vision Transformer for Crisis Image Classification
by Zijun Long, Richard McCreadie, Muhammad Imran
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); Social and Information Networks (cs.SI)
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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 The proposed paper develops a state-of-the-art deep neural model for automatic image classification and tagging in crisis response situations. Specifically, it adapts transformer-based architectures for crisis image classification, leveraging the Incidents1M dataset to train models that significantly outperform previous approaches in emergency type, image relevance, humanitarian category, and damage severity classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new approach for automatic image classification and tagging during crises using deep neural models. It adapts transformer-based architectures for crisis image classification, which can help authorities make more informed decisions with the aid of citizen-generated social media content. |
Keywords
» Artificial intelligence » Classification » Image classification » Transformer
