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:

Blur Background

Segmented Background

AI-generated Background

Bias of Tools During Annotation

Color in Background

Random Background with Real Environment

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.

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