Keze Wang

TL;DR: This paper proposes a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner and incorporates deep convolutional neural networks into AL.

TL;DR: Flow guided recurrent neural encoder (FGRNE) is presented, an accurate and end-to-end learning framework for video salient object detection that significantly outperforms state-of-the-art methods on the public benchmarks of DAVIS and FBMS.

TL;DR: A novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals by naturally combining two recently rising techniques: active learning and self-paced learning, capable of automatically annotating new instances and incorporating them into training under weak expert recertification.

TL;DR: A Recurrent 3D Pose Sequence Machine (RPSM) is presented to automatically learn the image-dependent structural constraint and sequence-dependent temporal context by using a multi-stage sequential refinement.

TL;DR: A novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks, and incorporates latent temporal structure into the deep model, accounting for large temporal variations of diverse human activities.