Contrastive Learning of Visual Representations from Unlabeled Videos

This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...

The impact of image filters on machine classification and human perception of weather conditions

Nowadays with the growth of social media, users upload millions of photos in different platforms online. Researchers in the field of computer vision devote their time and effort to analyze images in order to gain valuable insight. Data<br/>analysis and classification can be impeded by different factors. One of which is the image filters that are...