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Call for Papers

Important Dates


Submission of Non-Archival Extended Abstracts (Up to 4 pages) June 1, 2018 (extended)
Notification of Acceptance June 3, 2018 (extended)
Each accepted paper will have a 2-min spotlight presentation

We encourage submissions for work that has already been accepted in other venues, as well as new, work-in-progress submissions. The paper must be at most 4 pages, including references (a 1- or 2-page extended abstract is also fine). Accepted papers will appear on this site (but not the IEEE or CVPR proceedings). Since the papers in this workshop are at most 4 pages long, they can also be submitted to future major conferences such as CVPR and NIPS. Please submit the camera-ready version to visionmeetscognition@gmail.com by June 1, 2018 (11:59PM AOE). Submissions need not be anonymized, and will be lightly reviewed.



Call for Extended Abstracts


Although we have seen a recent boost of sensors for different applications, e.g., virtual / augmented reality, autonomous driving, smart homes, Internet of Things, intelligent algorithms for processing and handling visual data in the backend are largely missing. One key challenge is that algorithms that can understand images, human behaviors, and social activities from sensors deployed in daily lives go beyond the traditional scope of image and scene understanding, and they are expected to be capable of answering queries much broader and deeper than “what is where”. The mission of this workshop is to (a) identify the key domains in modern computer vision; (b) formalize the computational challenges in these domains; and (c) provide promising frameworks to solve these challenges. In conjunction with CVPR 2018, the fourth Vision meets Cognition workshop will bring together researchers from computer vision, computer graphics, robotics and cognitive science, to advance computer vision systems to go beyond answering “what is where” in an image and to build a sophisticated understanding of an image about Functionality, Physics, Intentionality and Causality (FPIC). In effect, these abilities allow an observer to answer an almost limitless range of questions about an image using finite and general-purpose models. In the meanwhile, we also want to emphasize that FPIC is never meant to be an exclusive set of image and scene understanding problems. We welcome any scholars who share the same perspective but are working on different problems. Authors are invited to submit extended research abstracts on topics related to the theme of the workshop. Several key topics are:

- Representation of visual structure and commonsense knowledge
- Recognition of object function / affordances
- Physically grounded scene interpretation
- 3D scene acquisition, modeling and reconstruction
- Human-object-scene interaction
- Physically plausible pose / action modeling
- Reasoning about goals and intents of the agents in the scenes
- Causal models in vision
- Abstract knowledge learning and transferring
- Top-down and Bottom-up inference algorithms
- Related topics in cognitive science and visual perception
- Applications of FPIC to augmented and mixed reality