Publications

Physically plausible multi-person 3D motion generation (working title)

Wang, Y., Colin, M., et al. • Work in progress — targeting a CVPR 2027 submission • 2026

Co-author on ongoing research led by Yilin Wang at the University of Alberta's Vision & Learning Lab (Prof. Li Cheng). Music- and text-prompt-conditioned multi-person 3D dance generation; my contribution focuses on the physical plausibility of the motion (ground-plane estimation, contacts, foot-skating, interpenetration, physics-based metrics on AIOZ-GDANCE).

Performances and Explainability of ViT and CNN Architectures: An Empirical Study Using LIME, SHAP, and GradCam

Colin, M., Chraibi Kadoud, I. • RJCIA 2024 (PFIA), La Rochelle • 2024

In recent years, explainable AI has been presented as the main solution for building trust between users and AI systems. To investigate this hypothesis, we propose an empirical study on the link between the performance and explainability of four computer vision algorithms: ViT, ResNet50, VGG16 and InceptionV3. Our study uses three local explainability methods: LIME, SHAP and GradCam. We show that, while explainable AI can be a tool for challenging the artificial representation of an algorithm and its behavior, it can also present robustness problems or contradictory information that can undermine trust. Our results show that by combining several explainability methods to explain a single prediction, it is possible to verify the reliability of the explanations and the information extracted.

My contribution: experimental protocol design, implementation of the models and explainability methods, analysis of the results, and writing. Written in my own time, teaching myself the scientific method.

@inproceedings{colin2024performances,
  title     = {Performances et explicabilit{\'e} de ViT et d'architectures CNN : une {\'e}tude empirique utilisant LIME, SHAP et GradCam},
  author    = {Colin, M{\'e}lissa and Chraibi Kadoud, Ikram},
  booktitle = {RJCIA 2024, Plateforme Fran{\c{c}}aise pour l'Intelligence Artificielle (PFIA)},
  year      = {2024},
  address   = {La Rochelle, France},
  url       = {https://hal.science/hal-04641791v1}
}