End-to-End Chess Recognition

Chess recognition refers to the task of identifying the chess pieces configuration from a chessboard image. Contrary to the predominant approach that aims to solve this task through the pipeline of chessboard detection, square localization, and piece classification, we rely on the power of deep learning models and introduce two novel...

Using and Abusing Equivariance: Investigating Differences between Exact and Approximate Equivariance in Computer Vision

In this work we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to their symmetries. We focus on the 2D roto-translation group and investigate the impact of broken equivariance on network performance. We show that changing the input dimension of a network by as little as a single pixel can...

PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations

A good action proposal method should generate proposals with high recall and high temporal overlap with groundtruth. The quality of the proposals relies on the labeled data available during training. Obtaining labeled data for untrimmed videos is a time consuming, expensive and error-prone task. The labels obtained are also subjective and the...

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...