We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g. young, short hair, wearing suit) 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.

本文介绍了一种扩展部件模型（EPM），用于在静态图像中识别人物属性（例如年轻、短发、穿着西装）和行为（例如奔跑、跳跃）。EPM仅使用部分部件评分，忽略冗余和随机背景，并通过算法从大量候选部件中自动挖掘部件并学习相应的鉴别模板及其位置。在三个挑战性的人体属性和动作数据集上验证了该方法，并得到了令人信服的定性和定量结果。

静态图像中描述人类语义的拓展部件模型