3 papers
cs.LG2024
Targeting Negative Flips in Active Learning using Validation Sets
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second wa…
cs.CV2024
HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms
Kiran Kokilepersaud, Seulgi Kim, Mohit Prabhushankar +1
In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during…
cs.CV2024
Benchmarking Human and Automated Prompting in the Segment Anything Model
Jorge Quesada, Zoe Fowler, Mohammad Alotaibi +2
The remarkable capabilities of the Segment Anything Model (SAM) for tackling image segmentation tasks in an intuitive and interactive manner has sparked interest in the design of e…