Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

The way that information propagates in neural networks is of great importance. In this paper,
we propose Path Aggregation Network (PANet) aiming at boosting information flow in …

We present a simple, fully-convolutional model for real-time instance segmentation that
achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is …

We present a conceptually simple, flexible, and general framework for object instance
segmentation. Our approach efficiently detects objects in an image while simultaneously …

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 …

Feature pyramids are a basic component in recognition systems for detecting objects at
different scales. But pyramid representations have been avoided in recent object detectors …

In this work we address the task of semantic image segmentation with Deep Learning and
make three main contributions that are experimentally shown to have substantial practical …

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 …

Cascade is a classic yet powerful architecture that has boosted performance on various
tasks. However, how to introduce cascade to instance segmentation remains an open …