In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

With the development of Internet technology and the popularity of digital devices, Content-
Based Image Retrieval (CBIR) has been quickly developed and applied in various fields …

Deep metric learning papers from the past four years have consistently claimed great
advances in accuracy, often more than doubling the performance of decade-old methods. In …

Existing metric learning losses can be categorized into two classes: pair-based and proxy-
based losses. The former class can leverage fine-grained semantic relations between data …

Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer
vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre …

Distance metric learning (DML) is to learn the embeddings where examples from the same
class are closer than examples from different classes. It can be cast as an optimization …

Exploiting multi-scale representations is critical to improve edge detection for objects at
different scales. To extract edges at dramatically different scales, we propose a Bi …

The generalization capability of neural networks across domains is crucial for real-world
applications. We argue that a generalized object recognition system should well understand …

Deep embeddings answer one simple question: How similar are two images? Learning
these embeddings is the bedrock of verification, zero-shot learning, and visual search. The …

Search in social networks such as Facebook poses different challenges than in classical
web search: besides the query text, it is important to take into account the searcher's context …