# Generic Object Detection With Dense Neural Patterns and Regionlets

Source: [Generic Object Detection With Dense Neural Patterns and Regionlets](<https://www.sota2.com/research/paper/68fa61501e209131ce9f3bde>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:57:35.894Z

Authors: Will Y. Zou, Xiaoyu Wang, Miao Sun, Yuanqing Lin

Published: 2014-04-16T17:23:47.000Z

## Abstract

This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection. We introduce Dense Neural Patterns, short for DNPs, which are dense local features derived from discriminatively trained deep convolutional neural networks. DNPs can be easily plugged into conventional detection frameworks in the same way as other dense local features(like HOG or LBP). The effectiveness of the proposed approach is demonstrated with the Regionlets object detection framework. It achieved 46.1% mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL VOC 2010 dataset, which dramatically improves the original Regionlets approach without DNPs.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1404.4316>)

[PDF](<https://arxiv.org/pdf/1404.4316.pdf>)