Deep learning

Pattern recognition (psychology)

Archaeology

Pure mathematics

Feature (linguistics)

Mathematics

Cognitive neuroscience of visual object recognition

Politics

Machine learning

Philosophy

Political science

Artificial intelligence

Geography

Object detection

Representation (politics)

Learning object

Context (archaeology)

Computer science

Linguistics

Object (grammar)

Field (mathematics)

Feature learning

International Journal of Computer Vision

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

Deep Learning for Generic Object Detection: A Survey