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cs.CV2022
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…
cs.CV2022
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.…