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
UnrealNAS: Can We Search Neural Architectures with Unreal Data?
Zhen Dong, Kaicheng Zhou, Guohao Li, Qiang Zhou, Mingfei Guo, Bernard Ghanem, Kurt Keutzer, Shanghang Zhang
Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images
Pallavi Jain, Bianca Schoen-Phelan, Robert Ross
Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes
Samrudhdhi B. Rangrej, Chetan L. Srinidhi, James J. Clark
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, Aaron Courville
Proper Reuse of Image Classification Features Improves Object Detection
Cristina Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin