Jun 15, 2019
Research at the intersection of vision and language has been attracting a lot of attention in recent years. Topics include the study of multi-modal representations, translation between modalities, bootstrapping of labels from one modality into another, visually-grounded question answering, segmentation and storytelling, and grounding the meaning of language in visual data. An ever-increasing number of tasks and datasets are appearing around this recently-established field. At NeurIPS 2018, we released the How2 data-set, containing more than 85,000 (2000h) videos, with audio, transcriptions, translations, and textual summaries. We believe it presents an ideal resource to bring together researchers working on the previously mentioned separate tasks around a single, large dataset. This rich dataset will facilitate the comparison of tools and algorithms, and hopefully foster the creation of additional annotations and tasks. We want to foster discussion about useful tasks, metrics, and labeling techniques, in order to develop a better understanding of the role and value of multi-modality in vision and language. We seek to create a venue to encourage collaboration between different sub-fields, and help establish new research directions and collaborations that we believe will sustain machine learning research for years to come.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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