art AIGC XAI image-classification zh en de arxiv:2310.08182 license:cc-by-nc-nd-4.0 region:us
---
license: cc-by-nc-nd-4.0
language:
- zh
- en
- de
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- art
- AIGC
- XAI
---

Welcome to the Explanatory AI Synthetic Dataset, where we delve into the significant role of backgrounds in enhancing object recognition tasks.
Our research builds upon the foundation laid by "Noise or Signal: The Role of Image Backgrounds in Object Recognition" (Xiao et al., ICLR 2022), "Explainable AI: Object Recognition With Help From Background" (Qiang et al., ICLR Workshop 2022), reinforced the notion that models trained solely on backgrounds can substantially improve accuracy. One noteworthy discovery highlighted in their studies is that more accurate models tend to rely less on backgrounds.
We categorize the elements present in the background into two main domains:

- Class-Independent Factors: These elements are not specific to certain classes and exhibit properties commonly found across the entire image dataset, such as colors and background edges.
- Class-Dependent Factors: These elements pertain to unique aspects of the class depicted in the background. Examples include shadows, reflections, land or sea backgrounds, classes often co-occurring with non-target classes, and the relative size of the class object in the background.

Our current focus is on comprehending the influence of image backgrounds on Computer Vision ML models, particularly in the realms of Detection and Classification. Inspired by the work, "Explainable AI: Object Recognition With Help From Background" ICLRw 2022, we aim to expand our dataset and explore the following topics:

![AI-generated-1.png](https://cdn-uploads.huggingface.co/production/uploads/653e5cb96426f79e297aff34/KuEg_ITV5ZVMihtd62eko.png)

Current Progress:
- ~~**Blur Background**-> **Done**!~~ You can find the image Generated in the corresponding folder!
- ~~**Segmented Background** -> **Done**!~~ you can download the image and its corresponding transparent mask image!
- ~~**Color in Background**->**Done**!~~ you can now download the image with different background color modified, and play with different color-ed images!
- ~~**Random Background with Real Environment** -> **Done**!~~ you can also find we generate the image with the photographer's real image as a background and remove the original background of the target object, but similar to the style!
- ~~**Bias of tools during annotation**-> **Done**!~~ for this one, you won't get a new image, because this is about math and statistics data analysis when different tools and annotators are applied!
- ~~**AI generated Background**-> **current on progress** ( 12 /12) **Done**!~~, So basically you can find one sample folder image we uploaded, please take a look at how real it is, and guess what LLM model we are using to generate the high-resolution background to make it so real :)

Project Website: [Project](https://sites.google.com/view/ximagenet-12/home)
Paper: [Paper](https://arxiv.org/abs/2310.08182)


Furthermore, we've devised a mathematical equation for the Robustness Score Scheme based on our dataset.
If you are interested in collaborating with us or learning more about our research project, please don't hesitate to reach out. Your contributions and insights are highly valued as we continue to advance our understanding of the intricate relationship between image backgrounds and AI models.

Stay tuned for more exciting updates! Our dataset currently comprises 12 classes, exceeding 23 GB in size, and boasting nearly 200K images. The meticulous process of manually generating these GenAI backgrounds spanned over one and a half years. We extend our heartfelt appreciation to all the contributors who dedicated their time and expertise to assist in labeling and the remarkable generation work.