text: We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g., young, short hair, wearing suits) and actions (e.g., running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e., a mixture of) ‘average’ templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e., it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.
  @cite_75: We present a novel approach to modeling human pose, together with interacting objects, based on compositional models of local visual interactions and their relations. Skeleton models, while flexible enough to capture large articulations, fail to accurately model self-occlusions and interactions. Poselets and Visual Phrases address this limitation, but do so at the expense of requiring a large set of templates. We combine all three approaches with a compositional model that is flexible enough to model detailed articulations but still captures occlusions and object interactions. Unlike much previous work on action classification, we do not assume test images are labeled with a person, and instead present results for "action detection" in an unlabeled image. Notably, for each detection, our model reports back a detailed description including an action label, articulated human pose, object poses, and occlusion flags. We demonstrate that modeling occlusion is crucial for recognizing human-object interactions. We present results on the PASCAL Action Classification challenge that shows our unified model advances the state-of-the-art for detection, action classification, and articulated pose estimation.
  @cite_84: We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.
summary: In mixture of components models, the training images are usually assigned to a single component (see Fig. for an illustration) and thus contribute to training one of the templates only. Such clustering like property limits their capability to generate novel articulations, as sub-articulation in different components cannot be combined. Such clustering and averaging are a form of regularization and involve manually setting the number of parts and components. In comparison, the proposed EPM does not enforce similar averaging, nor does it forbid it by definition. It can have a large number of parts (up to the order of the number of training images) if found necessary despite sufficient regularization. Part-based deformable models initialize the parts either with heuristics ( regions with high average energy @cite_84 ) or use annotations @cite_75 , while EPM systematically explores parts at a large number of locations, scales and atomicities and selects the ones best suited for the task.