Texts as Lines: Text Detection with Weak Supervision

Simultaneous Object Detection and Ranking with Weak Supervision

A standard approach to learning object category detectors is to provide strong supervision in the form of a region of interest (ROI) specifying each instance of the object in the training images [17]. In this work are goal is to learn from heterogeneous labels, in which some images are only weakly supervised, specifying only the presence or absence of the object or a weak indication of object l...

Towards Understanding Situated Text: Concept Labeling and Weak Supervision

Much of the focus of the natural language processing community lies in solving syntactic or semantic tasks with the aid of sophisticated machine learning algorithms and the encoding of linguistic prior knowledge. One of the most important features of natural languages is that their real-world use (as a tool for humans) is to communicate something about our physical reality or metaphysical consi...

Visual Parsing with Weak Supervision

Multi-Resolution Language Grounding with Weak Supervision

Language is given meaning through its correspondence with a world representation. This correspondence can be at multiple levels of granularity or resolutions. In this paper, we introduce an approach to multi-resolution language grounding in the extremely challenging domain of professional soccer commentaries. We define and optimize a factored objective function that allows us to leverage discou...

Learning Transformation Invariant Representations with Weak Supervision

Benjamin Coors1,2, Alexandru Condurache2, Alfred Mertins3 and Andreas Geiger1,4 1Autonomous Vision Group, MPI for Intelligent Systems, Tübingen, Germany 2Robert Bosch GmbH, Leonberg, Germany 3Institute for Signal Processing, University of Lübeck, Germany 4Computer Vision and Geometry Group, ETH Zürich, Switzerland {benjamin.coors,andreas.geiger}@tue.mpg.de, [email protected], m...