ChaLearn Workshop on Explainable Computer Vision Multimedia and Job Candidate Screening Coopetition @CVPR17
July 26, 2017
Aims and scope: Research progress in computer vision and pattern recognition has lead to a variety of modeling techniques with (almost) human-like performance in a variety of tasks. A clear example of this type of models are neural networks, whose deep variants dominate the arena of computer vision among other fields. Although this type of models have obtained astounding results in a variety of tasks they are limited in their explainability and interpretability. We are organizing a workshop and a competition on explainable computer vision systems. We aim to compile the latest efforts and research advances from the scientific community in enhancing traditional computer vision and pattern recognition algorithms with explainability capabilities at both the learning and decision stages.
Workshop topics and guidelines: The scope of the workshop comprises all aspects of explainability of learning machines. Including but not limited to the following topics:
Explainability of all aspects of computer vision and pattern recognition techniques for classification, regression, clustering, feature selection & extraction, ensemble learning, deep learning, etc.
Generation of explanations from the outputs of traditional computer vision and pattern recognition techniques.
Explainability of learned (trained) models for specific tasks.
Training, learning procedures leading to explainable models.
Natural language explanations of decisions taken by learning machines.
Image and video captioning.