Supervised ImageNet
Supervised ImageNet research focuses on improving image classification models by leveraging the massive ImageNet dataset. Current efforts concentrate on enhancing data curation strategies, developing more efficient training methods (including exploring alternative architectures like binary neural networks and leveraging self-supervised learning), and addressing challenges like dataset bias and the need for explainable AI. These advancements are crucial for improving the accuracy, efficiency, and trustworthiness of computer vision systems across various applications, from medical imaging to agricultural technology.
Papers
Refining Skewed Perceptions in Vision-Language Models through Visual Representations
Haocheng Dai, Sarang Joshi
LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate
Anthony Fuller, Daniel G. Kyrollos, Yousef Yassin, James R. Green
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
Mohit Prabhushankar, Ghassan AlRegib