# Multi-scale Volumes for Deep Object Detection and Localization

arXiv: https://arxiv.org/abs/1505.03597 · HTML: https://mlsanity.com/paper/1505.03597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2015.

## Built on by (most cited first, 1 of 1)

- [Counting and Locating High-Density Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2102.04366/) (2021)

## Abstract (arXiv)

This study aims to analyze the benefits of improved multi-scale reasoning for object detection and localization with deep convolutional neural networks. To that end, an efficient and general object detection framework which operates on scale volumes of a deep feature pyramid is proposed. In contrast to the proposed approach, most current state-of-the-art object detectors operate on a single-scale in training, while testing involves independent evaluation across scales. One benefit of the proposed approach is in better capturing of multi-scale contextual information, resulting in significant gains in both detection performance and localization quality of objects on the PASCAL VOC dataset and a multi-view highway vehicles dataset. The joint detection and localization scale-specific models are shown to especially benefit detection of challenging object categories which exhibit large scale variation as well as detection of small objects.