Members from CVC are organising a total number of three workshops at ECCV 2016 within three different areas:
IWRR aims at bringing together computer vision researchers and practitioners with an interest in reading systems that operate on images acquired in unconstrained conditions, such as scene images and video sequences, born-digital images, wearable camera and lifelog feeds, social media images, etc. The particular focus of the workshop is on the automatic extraction and interpretation of textual content in images, and applications that use textual information obtained automatically by such methods.
This topic is gaining momentum in the computer vision community as new datasets are being released and the application areas are being diversified. The workshop aims to offer a forum for researchers to share experiences and latest results in the area, and to discuss current trends and the evolution of the Robust Reading Competition (RRC) which has attracted to date over 1,500 researchers and more than four thousand submissions. Reporting recent results on standard datasets such as the ICDAR RRC 2015 dataset on incidental scene text or the Coco-Text dataset is particularly encouraged.
Workshop and Challenge on Apparent Personality Analysis, organised by Dr. Sergio Escalera, and will take place on the 9th of October.
The proposed workshop aims at studying all aspects of computer vision and pattern recognition devoted to the analysis of human personality from images and videos. Co-located with the workshop there is a challenge on first impressions, that is, recognizing personality traits for users after seeing a short video.
The Virtual/Augmented Reality for Visual Artificial Intelligence (VARVAI) workshop, organised by Dr. Antonio López, and will take place on the 16th of October.
The purpose of this workshop is to provide a forum to gather researchers around the nascent field of Virtual/Augmented Reality (VR/AR or just VAR) used for data generation in order to learn and study VAI algorithms. VAR technologies have made impressive progress recently, in particular in the fields of computer graphics, physics engines, game engines, authoring tools, or hardware, thanks to a strong push from various big players in the industry (including Facebook/Oculus, Google, Sony/Playstation, Valve, and Unity Technologies).
Although mostly designed for multimedia applications geared towards human entertainment, more and more researchers (cf. references below) have noticed the tremendous potential that VAR platforms hold as data generation tools for algorithm/AI consumption. In light of the long-standing history of synthetic data in computer vision and multimedia, VAR technologies represent the next step of multimedia data generation, vastly improving on the quantity, variety, and realism of densely and accurately labeled fine-grained data that can be generated, and needed to push the scientific boundaries of research on AI.