# A Classification Leveraged Object Detector

Source: [A Classification Leveraged Object Detector](<https://www.sota2.com/research/paper/68fa62671e209131ce9f926f>)

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

Data updated: 2025-10-25T20:19:32.265Z

Authors: Miao Sun, Tony X. Han, Zhihai He

Published: 2016-04-07T01:11:50.000Z

## Abstract

Currently, the state-of-the-art image classification algorithms outperform the best available object detector by a big margin in terms of average precision. We, therefore, propose a simple yet principled approach that allows us to leverage object detection through image classification on supporting regions specified by a preliminary object detector. Using a simple bag-of- words model based image classification algorithm, we leveraged the performance of the deformable model objector from 35.9% to 39.5% in average precision over 20 categories on standard PASCAL VOC 2007 detection dataset.

## 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/1604.01841>)

[PDF](<https://arxiv.org/pdf/1604.01841.pdf>)