37 citations · 129 across the 31 of their papers we have counts for
5 papers · 1 filter
Mitigating Sampling Bias and Improving Robustness in Active Learning
Ranganath Krishnan, Alok Sinha, Nilesh Ahuja +3
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness. We introduce supervise…
Robust Contrastive Active Learning with Feature-guided Query Strategies
Ranganath Krishnan, Nilesh Ahuja, Alok Sinha +3
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal comp…
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data
Shashank Bujimalla, Mahesh Subedar, Omesh Tickoo
In this paper, we propose an approach to improve image captioning solution for images with novel objects that do not have caption labels in the training dataset. We refer to our ap…
Data augmentation to improve robustness of image captioning solutions
Shashank Bujimalla, Mahesh Subedar, Omesh Tickoo
In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution, and notice a degradation i…
Energy-Based Anomaly Detection and Localization
Ergin Utku Genc, Nilesh Ahuja, Ibrahima J Ndiour +1
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem. In this setup, we have acces…