The ICML 2013 Workshop on Challenges in Representation Learning(1) focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.

Abstract. The ICML 2013 Workshop on Challenges in Representation Learning3 focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.

Abstract. In this paper, we introduce the CrowdTruth open-source software framework for machine-human computation, that implements a novel approach to gathering human annotation data in a wide range of annotation tasks and on a variety of media (e.g. text, images, videos). The CrowdTruth approach captures human semantics through a pipeline of three processes: a) combining various machine processing of text, image and video in order to understand better the input content and optimise its suitability for micro-tasks, thus optimise the time and cost of the crowdsourcing process; b) providing reusable human-computing task templates to collect the maximum diversity in the human interpretation, thus collect richer human semantics; and c) implementing 'disagreement metrics', i.e. CrowdTruth metrics, to support deep analysis of the quality and semantics of the crowdsourcing data. Instead of the traditional inter-annotator agreement, we use their disagreement as a useful signal to evaluate the data quality, ambiguity, and vagueness. In this paper we demonstrate the innovative CrowdTruth approaches embodied in the software to: 1) support processing of different text, image and video data; 2) support a variety of annotation tasks; 3) harness worker disagreement with CrowdTruth metrics; and 4) provide an interface to support data analysis and visualisation. In previous work we introduced the CrowdTruth methodology with examples for semantic interpretation of medical text for relation and factor extraction, and with newspaper text for event extraction. In this paper, we demonstrate the applicability and robustness of the approach to a wide variety of problems across a number of domains. We also show the advantages of using open standards and the extensibility of the framework with new data modalities and annotation tasks, as well as its openness to external services.

We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder. Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.

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