The comparative losses (typically, triplet loss) are appealing choices for learning person re-
identification (ReID) features. However, the triplet loss is computationally much more …

Person re-identification (Re-ID) is a challenging task in the field of computer vision and
focuses on matching people across images from different cameras. The extraction of robust …

State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-
class classification framework. Despite being popular and effective, these methods still have …

Surveillance systems, in spite of the recent advances, still poses many challenges,
especially in the field of patrolling or tracking of subjects through CCTV footage or any other …

Visible infrared person re-identification (VI-reid) has gradually increased in popularity as an
crucial branch of person re-identification (reid). It not only has intra-class variations caused …

Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be
collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) …

In this paper, we focus on a general yet important learning problem, pairwise similarity
learning (PSL). PSL subsumes a wide range of important applications, such as open-set …

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-
art schemes are typically based upon convolutional neural network (CNN) features. Despite …

Abstract Software Defect Prediction (SDP) aims to detect defective modules to enable the
reasonable allocation of testing resources, which is an economically critical activity in …

We explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic
scenario of FSL, in which a test instance might not belong to any of the target categories …