This paper describes an energy-based learning method that predicts the activities of multiple agents simultaneously. It aims to forecast both upcoming actions and paths of all agents in a scene based on their past activities, which can be jointly formulated by a probabilistic model over time. Learning this model is challenging because: 1) it has a large number of time-dependent variables that must scale with the forecast horizon and the number of agents; 2) distribution functions have to contain multiple modes in order to capture the spatio-temporal complexities of each agent's activities. To address these challenges, we put forth a novel Energy-based Learning approach for Multi-Agent activity forecasting (ELMA) to estimate this complex model via maximum log-likelihood estimation. Specifically, by sampling from a sequence of factorized marginalized multi-model distributions, ELMA generates most possible future actions efficiently. Moreover, by graph-based representations, ELMA also explicitly resolves the spatio-temporal dependencies of all agents' activities in a single pass. Our experiments on two large-scale datasets prove that ELMA outperforms recent leading studies by an obvious margin.

This paper presents a precise, stable, and invertible reflectance model for photometric stereo. This microfacet-based model is applicable to all types of isotropic surface reflectance, covering cases from diffusion to specular reflections. We introduce a single variable to physically quantify the surface smoothness, and by monotonically sliding this variable between 0 and 1, our model enables a versatile representation that can smoothly transform between an ellipsoid of revolution and the equation for Lambertian reflectance. In the inverse domain, this model offers a compact and physically interpretable formulation, for which we introduce a fast and lightweight solver that allows accurate estimations for both surface smoothness and surface shape. Finally, extensive experiments on the appearances of synthesized and real objects evidence that this model is state-of-the-art in our off-the-shelf solution.

Although person Re-Identification (ReID) is widely applied in a variety of multimedia systems, most of its essentially multifaceted output is evaluated and visualized inflexibly only using a list of images ranked by the similarity of image content, while the correlations between samples of different IDs and the spatial-temporal features of the images are underinvestigated. As system operators need a comfortable access to these important elements, we introduce an interactive design of a person ReID system to visualize these quantities. We demonstrate that a system offering these visual representations can effectively expedite and improve a person re-identification analysis and make it a much user-friendly experience.

A precise, stable and invertible model for surface reflectance is the key to the success of photometric stereo with real world materials. Recent developments in the field have enabled shape recovery techniques for surfaces of various types, but an effective solution to directly estimating the surface normal in the presence of highly specular reflectance remains elusive. In this paper, we derive an analytical isotropic microfacet-based reflectance model, based on which a physically interpretable approximate is tailored for highly specular surfaces. With this approximate, we identify the equivalence between the surface recovery problem and the ellipsoid of revolution fitting problem, where the latter can be described as a system of polynomials. Additionally, we devise a fast, non-iterative and globally optimal solver for this problem. Experimental results on both synthetic and real images validate our model and demonstrate that our solution can stably deliver superior performance in its targeted application domain.

The rapid scaling up of Networked Control Systems (NCS) is forcing traditional single-hop shared medium industrial fieldbuses (a.k.a. fieldbuses) to evolve toward multi-hop switched fieldbuses. Such evolution faces many challenges. The first is the re-design of switch architecture. To meet the real-time nature of NCS traffic, and to lay a smooth evolution path for switch manufacturers, it is widely agreed that a (if not the) promising switch architecture is an input queueing crossbar architecture running TDMA scheduling. The second challenge is real-time multicast. NCS applications usually involve complex distributed multiple-input-multiple-output interactions, which by their nature necessitate real-time multicast. In shared medium fieldbuses, real-time multicast is straightforward as data sent to the medium is heard by all nodes. On multi-hop switched fieldbuses, however, real-time multicast becomes non-trivial. In this paper, we prove real-time multicast on multi-hop switched fieldbuses is NP-Hard. What is more, real-time multicast on multi-hop switched fieldbuses is fundamentally different from Internet multicast, due to real-time requirement and the homogeneous input queueing crossbar switch architecture. Particularly, switch external links' capacities are no longer mutually independent. Such drastic change of assumptions warrants developing new routing algorithms, and a heuristic algorithm is hereby proposed.