Scale-aware Pixel-wise Object Proposal Networks

TL;DR

The paper introduces a scale-aware pixel-wise object proposal network that improves localization accuracy, especially for small objects, and enhances object detection performance with minimal extra computation.

Contribution

A novel scale-aware pixel-wise proposal network with specialized localization networks and adaptive combination for better small object detection.

Findings

Outperforms state-of-the-art models on PASCAL VOC 2007

Significantly improves mAP in Fast-RCNN detection

Demonstrates strong generalization on ILSVRC 2013

Abstract

Object proposal is essential for current state-of-the-art object detection pipelines. However, the existing proposal methods generally fail in producing results with satisfying localization accuracy. The case is even worse for small objects which however are quite common in practice. In this paper we propose a novel Scale-aware Pixel-wise Object Proposal (SPOP) network to tackle the challenges. The SPOP network can generate proposals with high recall rate and average best overlap (ABO), even for small objects. In particular, in order to improve the localization accuracy, a fully convolutional network is employed which predicts locations of object proposals for each pixel. The produced ensemble of pixel-wise object proposals enhances the chance of hitting the object significantly without incurring heavy extra computational cost. To solve the challenge of localizing objects at small scale, two localization networks which are specialized for localizing objects with different scales are introduced, following the divide-and-conquer philosophy. Location outputs of these two networks are then adaptively combined to generate the final proposals by a large-/small-size weighting network. Extensive evaluations on PASCAL VOC 2007 show the SPOP network is superior over the state-of-the-art models. The high-quality proposals from SPOP network also significantly improve the mean average precision (mAP) of object detection with Fast-RCNN framework. Finally, the SPOP network (trained on PASCAL VOC) shows great generalization performance when testing it on ILSVRC 2013 validation set.