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20172024
most citedMasked Transformer for image Anomaly Localization

36 citations · 64 across the 11 of their papers we have counts for

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14 papers · 1 filter

cs.CV202236 cited

Masked Transformer for image Anomaly Localization

Axel De Nardin, Pankaj Mishra, Gian Luca Foresti +1

Image anomaly detection consists in detecting images or image portions that are visually different from the majority of the samples in a dataset. The task is of practical importanc…

cs.CV2022

Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application

Danilo Avola, Luigi Cinque, Alessio Fagioli +4

Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong…

cs.CV202214 cited

Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals

Danilo Avola, Marco Cascio, Luigi Cinque +2

The increasing availability of wireless access points (APs) is leading towards human sensing applications based on Wi-Fi signals as support or alternative tools to the widespread v…

cs.CV20218 cited

Drone swarm patrolling with uneven coverage requirements

Claudio Piciarelli, Gian Luca Foresti

Swarms of drones are being more and more used in many practical scenarios, such as surveillance, environmental monitoring, search and rescue in hardly-accessible areas, etc.. While…

cs.CV2021

VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization

Pankaj Mishra, Riccardo Verk, Daniele Fornasier +2

We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use…

cs.CV20202 cited

Is It a Plausible Colour? UCapsNet for Image Colourisation

Rita Pucci, Christian Micheloni, Gian Luca Foresti +1

Human beings can imagine the colours of a grayscale image with no particular effort thanks to their ability of semantic feature extraction. Can an autonomous system achieve that? C…