17 citations · 36 across the 18 of their papers we have counts for
5 papers · 2 filters
Learning Saliency From Fixations
Yasser Abdelaziz Dahou Djilali, Kevin McGuiness, Noel O'Connor
We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps. Models typically rely on co…
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
Ayush K. Rai, Tarun Krishna, Feiyan Hu +4
Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos w…
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts
Mayug Maniparambil, Chris Vorster, Derek Molloy +3
Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs a…
Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data
Enric Moreu, Eric Arazo, Kevin McGuinness +1
Early detection of colorectal polyps is of utmost importance for their treatment and for colorectal cancer prevention. Computer vision techniques have the potential to aid professi…
Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention
Enric Moreu, Eric Arazo, Kevin McGuinness +1
Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annot…