1 citations · 1 across the 2 of their papers we have counts for
5 papers
Active Learning for Vision-Language Models
Bardia Safaei, Vishal M. Patel
Pre-trained vision-language models (VLMs) like CLIP have demonstrated impressive zero-shot performance on a wide range of downstream computer vision tasks. However, there still exi…
Gradient-Regularized Out-of-Distribution Detection
Sina Sharifi, Taha Entesari, Bardia Safaei +2
One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addr…
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling
Wele Gedara Chaminda Bandara, Vishal M. Patel
In this paper, we introduce Attention Prompt Tuning (APT) - a computationally efficient variant of prompt tuning for video-based applications such as action recognition. Prompt tun…
Entropic Open-set Active Learning
Bardia Safaei, Vibashan VS, Celso M. de Melo +1
Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive perfo…
Guarding Barlow Twins Against Overfitting with Mixed Samples
Wele Gedara Chaminda Bandara, Celso M. De Melo, Vishal M. Patel
Self-supervised Learning (SSL) aims to learn transferable feature representations for downstream applications without relying on labeled data. The Barlow Twins algorithm, renowned…