In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks
that use binary values for activations and weights, instead of full precision values. With …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

In this paper, we investigate the issue of knowledge distillation for training compact semantic
segmentation networks by making use of cumbersome networks. We start from the …

Abstract Knowledge distillation aims at transferring knowledge acquired in one model (a
teacher) to another model (a student) that is typically smaller. Previous approaches can be …

Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …

State-of-the-art CNN based recognition models are often computationally prohibitive to
deploy on low-end devices. A promising high level approach tackling this limitation is …

In this paper, we propose a novel method called AlignedReID that extracts a global feature
which is jointly learned with local features. Global feature learning benefits greatly from local …

Person re-identification is a challenging retrieval task that requires matching a person's
acquired image across non-overlapping camera views. In this paper we propose an effective …