9 citations · 10 across the 5 of their papers we have counts for
5 papers
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision
Tzu-Jui Julius Wang, Jorma Laaksonen, Tomas Langer +2
Weakly-supervised vision-language (V-L) pre-training (W-VLP) aims at learning cross-modal alignment with little or no paired data, such as aligned images and captions. Recent W-VLP…
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection
Mohamed Yousef, Marcel Ackermann, Unmesh Kurup +1
Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples.…
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold
Mohamed Yousef, Tom E. Bishop
Text recognition is a major computer vision task with a big set of associated challenges. One of those traditional challenges is the coupled nature of text recognition and segmenta…
Learning to hash with semantic similarity metrics and empirical KL divergence
Heikki Arponen, Tom E. Bishop
Learning to hash is an efficient paradigm for exact and approximate nearest neighbor search from massive databases. Binary hash codes are typically extracted from an image by round…
SHREWD: Semantic Hierarchy-based Relational Embeddings for Weakly-supervised Deep Hashing
Heikki Arponen, Tom E Bishop
Using class labels to represent class similarity is a typical approach to training deep hashing systems for retrieval; samples from the same or different classes take binary 1 or 0…