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This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users’ needs. We propose to localize activities...
Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e.g. human actions) segments from untrimmed videos is an important step for large-scale video analysis. We propose a novel Temporal Unit Regression Network (TURN) model. There are two salient aspects of TURN: (1) TURN jointly predicts action proposals and refines the temporal...
The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples...
We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions...
A new method for Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification based on Deep Sparse Filtering Network (DSFN) is proposed in this paper. It uses a novel deep learning network to learn features from the input raw data automatically. And the spatial information between pixels on PolSAR image is combined into the input data. Moreover, unlike the conventional deep networks, the...
Event recognition has been an important topic in computer vision research due to its many applications. However, most of the work has focused on videos taken from a fixed camera, known environments and basic events. Here, we focus on classification of unconstrained, web videos into much higher level activities. We follow the approach of constructing fixed length feature vectors from local feature...
Today, iris recognition, fingerprint recognition, face recognition, voice recognition and other biometric technology are experiencing rapid development. This paper addresses a new biometric technology-optic fundus blood vessel skeleton recognition based on band tree. The green gray scale ocular fundus image is utilized. The skeleton feature of optic fundus blood vessel is extracted at first. After...
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