Computer Vision Model
Computer vision models aim to enable computers to "see" and interpret images, enabling applications ranging from medical diagnosis to autonomous driving. Current research emphasizes improving model robustness, addressing biases and ethical concerns in datasets, and enhancing explainability through techniques like class activation maps and contextual analysis. This field is crucial for advancing various scientific disciplines and practical applications, with ongoing efforts focused on improving accuracy, efficiency, and fairness across diverse datasets and tasks.
Papers
FACET: Fairness in Computer Vision Evaluation Benchmark
Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, Candace Ross
Njobvu-AI: An open-source tool for collaborative image labeling and implementation of computer vision models
Jonathan S. Koning, Ashwin Subramanian, Mazen Alotaibi, Cara L. Appel, Christopher M. Sullivan, Thon Chao, Lisa Truong, Robyn L. Tanguay, Pankaj Jaiswal, Taal Levi, Damon B. Lesmeister