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cs.HC2026
SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches
Wencan Zhang, Mario Michelessa, Xuejun Zhao +1
Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap. We…
cs.HC2025★ 1 cited
Varif.ai to Vary and Verify User-Driven Diversity in Scalable Image Generation
M. Michelessa, J. Ng, C. Hurter +1
Diversity in image generation is essential to ensure fair representations and support creativity in ideation. Hence, many text-to-image models have implemented diversification mech…
cs.HC2024
Incremental XAI: Memorable Understanding of AI with Incremental Explanations
Jessica Y. Bo, Pan Hao, Brian Y. Lim
Many explainable AI (XAI) techniques strive for interpretability by providing concise salient information, such as sparse linear factors. However, users either only see inaccurate…