The combination of these largely orthogonal dimensions can span a large space of image understanding.

Despite their apparent differences, these domains do connect with each other in ways that are theoretically important: (a) they usually don’t project onto explicit visual features; (b) existing computer vision algorithms are neither competent in these domains, nor (in most cases) applicable at all; and (c) human cognition is nevertheless highly efficient at these domains. Therefore, studying FPIC should significantly fill the gap between computer vision and human vision. On the one hand, human studies on FPIC-related topics can inspire the invention of novel, cognitively-motivated computer vision systems. On the other hand, state-of-the-art computer vision systems can expand the scope of cognitive sciences to address challenges in real scenes.

The introduction of FPIC will advance cognitive models 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 scenes can be transferred to novel situations; (b) small sample learning. Learning 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 boost the performance of each other as a result.

Several key topics are:

- Physically grounded scene interpretation

- Causal model of vision and cognition

- Human-object-scene interaction

- Human-robot collaboration

- Reasoning about goals and intents of the agents in the scenes

- Top-down and Bottom-up inference algorithms

- Related topics in cognitive science and visual perception

In conjunction with CogSci 2016, our “Physical and Social Scene Understanding” workshop will bring together researchers from cognitive science, computer vision and robotics, to illuminate cognitively-motivated vision systems going beyond labeling “what is where” in an image. These systems work closely together to achieve a sophisticated and coherent understanding of scenes with respect to Functionality, Physics, Intentionality and Causality (FPIC). In effect, these systems are expected to answer an almost limitless range of questions about an image using a finite and general-purpose model. In the meanwhile, we also want to highlight that FPIC is never meant to be an exclusive set of scene understanding problems. We welcome the insights of scholars who share the same perspective but are working on different problems.