My research and academic interests lie at the intersection of machine learning and computer vision. I’m exploring various theoretical frameworks and empirical techniques. Whether it’s traditional image processing techniques or advanced deep learning methodologies, I’m always on the lookout for the next challenge in computer vision. Feel free to explore my work and reach out for collaborations or discussions!
Accepted at 2 Workshops: Human-Centric Representation Learning Workshop at AAAI-24, Deployable AI Workshop at AAAI-24, 2024
The study introduces a concept learning framework, integrating an explanation generator via adversarial training, enhancing model interpretability for visual classification.
Published in: International Conference on Computer Vision 2023 (ICCV'23), 2023
The study introduces OSBORN, a novel metric for assessing the transferability of model ensembles to downstream tasks, considering image and task disparities as well as cohesiveness between models