Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

Autonomous vehicles are becoming central for the future of mobility, supported by advances
in deep learning techniques. The performance of aself-driving system is highly dependent …

Current semantic segmentation methods focus only on mining" local" context, ie,
dependencies between pixels within individual images, by context-aggregation modules …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

We present a novel hierarchical triplet loss (HTL) capable of automatically collecting
informative training samples (triplets) via a defined hierarchical tree that encodes global …

Convolutional neural network (CNN) has turned to be the state of the art for object detection
task of computer vision. In this chapter, we have reviewed some popular state-of-the-art …

Zero-shot learning has gained popularity due to its potential to scale recognition models
without requiring additional training data. This is usually achieved by associating categories …

The intensive annotation cost and the rich but unlabeled data contained in videos motivate
us to propose an unsupervised video-based person re-identification (re-ID) method. We start …

This paper addresses the task of learning an image clas-sifier when some categories are
defined by semantic de-scriptions only (eg visual attributes) while the others are defined by …

Self-supervised learning has recently begun to rival supervised learning on computer vision
tasks. Many of the recent approaches have been based on contrastive instance …