Object detection performance, as measured on the canonical PASCAL VOC Challenge
datasets, plateaued in the final years of the competition. The best-performing methods were …

Object detection performance, as measured on the canonical PASCAL VOC dataset, has
plateaued in the last few years. The best-performing methods are complex ensemble …

We present region-based, fully convolutional networks for accurate and efficient object
detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that …

We propose drl-RPN, a deep reinforcement learning-based visual recognition model
consisting of a sequential region proposal network (RPN) and an object detector. In contrast …

State-of-the-art object detection networks depend on region proposal algorithms to
hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced …

In this paper, we introduce a new large-scale object detection dataset, Objects365, which
has 365 object categories over 600K training images. More than 10 million, high-quality …

Most object detectors contain two important components: a feature extractor and an object
classifier. The feature extractor has rapidly evolved with significant research efforts leading …

Current two-stage object detectors, which consists of a region proposal stage and a
refinement stage, may produce unreliable results due to ill-localized proposed regions. To …

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for
object detection. Fast R-CNN builds on previous work to efficiently classify object proposals …

Object detection systems based on the deep convolutional neural network (CNN) have
recently made ground-breaking advances on several object detection benchmarks. While …