@article {1354, title = {Complexity of Representation and Inference in Compositional Models with Part Sharing}, number = {031}, year = {2015}, month = {05/2015}, abstract = {This paper performs a complexity analysis of a class of serial and parallel compositional models of multiple objects and shows that they enable efficient representation and rapid inference. Compositional models are generative and represent objects in a hierarchically distributed manner in terms of parts and subparts, which are constructed recursively by part-subpart compositions. Parts are represented more coarsely at higher level of the hierarchy, so that the upper levels give coarse summary descriptions (e.g., there is a horse in the image) while the lower levels represents the details (e.g., the positions of the legs of the horse). This hierarchically distributed representation obeys the executive summary principle, meaning that a high level executive only requires a coarse summary description and can, if necessary, get more details by consulting lower level executives. The parts and subparts are organized in terms of hierarchical dictionaries which enables part sharing between different objects allowing efficient representation of many objects. The first main contribution of this paper is to show that compositional models can be mapped onto a parallel visual architecture similar to that used by bio-inspired visual models such as deep convolutional networks but more explicit in terms of representation, hence enabling part detection as well as object detection, and suitable for complexity analysis. Inference algorithms can be run on this architecture to exploit the gains caused by part sharing and executive summary. Effectively, this compositional architecture enables us to perform exact inference simultaneously over a large class of generative models of objects.The second contribution is an analysis of the complexity of compositional models in terms of computation time (for serial computers) and numbers of nodes (e.g., {\textquoteleft}{\textquoteleft}neurons") for parallel computers. In particular, we compute the complexity gains by part sharing and executive summary and their dependence on how the dictionary scales with the level of the hierarchy. We explore three regimes of scaling behavior where the dictionary size (i) increases exponentially with the level of the hierarchy, (ii) is determined by an unsupervised compositional learning algorithm applied to real data, (iii) decreases exponentially with scale. This analysis shows that in some regimes the use of shared parts enables algorithms which can perform inference in time linear in the number of levels for an exponential number of objects. In other regimes part sharing has little advantage for serial computers but can enable linear processing on parallel computers.}, author = {Alan Yuille and Roozbeh Mottaghi} } @article {453, title = {Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts.}, number = {015}, year = {2014}, month = {06/2014}, abstract = {

Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different {\textquotedblleft}detectability{\textquotedblright} patterns caused by deformations, occlusion and/or low resolution.
We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1\% AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part.

}, author = {Xianjie Chen and Roozbeh Mottaghi and Xiaobai Liu and Sanja Fidler and Raquel Urtasun and Alan Yuille} } @article {454, title = {Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding.}, number = {020}, year = {2014}, month = {06/2014}, abstract = {

Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local appearance based classifiers. In this work, we are interested in understanding the roles of these different tasks in improved scene understanding, in particular semantic segmentation, object detection and scene recognition. Towards this goal, we {\textquotedblleft}plug-in{\textquotedblright} human subjects for each of the various components in a state-of-the-art conditional random field model. Comparisons among various hybrid human-machine CRFs give us indications of how much {\textquotedblleft}head room{\textquotedblright} there is to improve scene understanding by focusing research efforts on various individual tasks.

}, author = {Roozbeh Mottaghi and Sanja Fidler and Alan Yuille and Raquel Urtasun and Devi Parikh} }