Summary of Sensitive Image Classification by Vision Transformers, By Hanxian He et al.
Sensitive Image Classification by Vision Transformers
by Hanxian He, Campbell Wilson, Thanh Thi Nguyen, Janis Dalins
First submitted to arxiv on: 21 Dec 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 The proposed paper investigates the application of vision transformer models to classify child sexual abuse images, addressing the challenges posed by similar inter-class correlations and diverse intra-class correlations. The authors leverage the self-attention mechanism of vision transformers to navigate through image patches effectively, reducing ambiguity in attention maps and improving performance in computer vision tasks. The study constructs two datasets, one for clean and pornographic images and another with three classes including images indicative of pornography, sourced from Reddit and Google Open Images data. The authors compare the performance of various popular vision transformer models and traditional pre-trained ResNet models on an adult content image benchmark dataset, demonstrating that vision transformers surpass pre-trained models in classification and detection capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to help computers identify harmful images. One problem is that similar pictures can be very different, and different pictures can look quite alike. Vision transformer models are a type of artificial intelligence that can handle these complexities by looking at the whole picture and paying attention to important parts. The authors of this paper created two special collections of images: one with clean and dirty pictures, and another with three categories including pictures that might be explicit or violent. They used these datasets to test how well different types of computer vision models work in identifying harmful images. The results show that vision transformers are better at this task than other popular models. |
Keywords
» Artificial intelligence » Attention » Classification » Resnet » Self attention » Vision transformer
