We provide code support and configuration files to reproduce the results in the paper for "RepPoints: Point Set Representation for Object Detection" on COCO object detection. Our code is based on mmdetection, which is a clean open-sourced project for benchmarking object detection methods.
Introduction
RepPoints, initially described in arXiv, is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by bounding box + RoIPool (RoIAlign). The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a point set instead of a bounding box, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s spatial extent and enables semantically aligned feature extraction. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection.
Another feature of this repo is the demonstration of an anchor-free detector, which can be as effective as state-of-the-art anchor-based detection methods. The anchor-free detector can utilize either bounding box or RepPoints as the basic object representation.
9658791824Github: https://github.com/microsoft/RepPoints
Article: https://arxiv.org/abs/1904.11490