This paper presents a novel method for detecting and localizing objects of a visual category
in cluttered real-world scenes. Our approach considers object categorization and figure …

Datasets for training object recognition systems are steadily growing in size. This paper
investigates the question of whether existing detectors will continue to improve as data …

In this paper, we propose a multi-component approach for object detection. Rather than
attempting to represent an object category with a monolithic model, or pre-defining a …

We investigate how to iteratively and mutually boost object classification and detection
performance by taking the outputs from one task as the context of the other one. While …

Statistical-learning-based object detection is an important topic in computer vision. It learns
visual representation from annotated exemplars to identify semantic defined objects in …

Recent outstanding results of supervised object detection in competitions and challenges
are often associated with specific metrics and datasets. The evaluation of such methods …

Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

Background The goal of object detection is to detect all instances of objects from one or
several known classes, such as people, cars, or faces in an image. Typically only a small …

This paper addresses the task of efficient object class detection by means of the Hough
transform. This approach has been made popular by the Implicit Shape Model (ISM) and has …

While most steps in the modern object detection methods are learnable, the region feature
extraction step remains largely hand-crafted, featured by RoI pooling methods. This work …