VMC 2015 - Vision Meets Cognition Workshop

Despite their apparent differences, these domains do connect with each other in ways that are theoretically important. These connections include: (a) they usually don't project onto explicit visual features; (b) existing computer vision algorithms are either not competent in these domains, or (in most cases) not applicable at all; and (c) human vision is nevertheless highly efficient at these domains. Therefore, studying FPIC should significantly fill the gap between computer vision and human vision, which is not only for visual recognition, but also for reasoning of visual scene with common-sense knowledge.
The introduction of FPIC will advance a vision system in three aspects: (a) transfer learning. As higher-level representation, FPIC tends to be globally invariant across the entire human living space. Therefore, learning in one type of scene can be transferred to novel situations; (b) small sample learning. Leaning of FPIC, which is consistent and noise-free, is possible even without a wealth of previous experience or "big data"; and (c) bidirectional inference. Inference with FPIC requires the combination of top-down abstract knowledge and bottom-up visual patterns. The bidirectional processes can therefore boost each other as a result.
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 model in vision
- Abstract knowledge learning and transferring
- Top-down and Bottom-up inference algorithms
- Related topics in cognitive science and visual perception