UCLA

Local information is very crucial in many image and video analysis tasks. In this thesis,

we introduce four representative works in exploiting local information. We first introduce a

set of per-pixel labeling datasets, which provide a good platform for studies of using local

information in image analysis. Based on this dataset, we propose a novel segmentation

method which utilizes local appearance consistency for car semantic part parsing task. We

then address the attention issue in video action recognition tasks, by designing a latent

attention module, which is jointly learned with video recognition components. Last, we

improve the attention mechanism to explicitly detect spatial and spatio-temporal regions

that are related to actions (ROIs).