# 1-HKUST: Object Detection in ILSVRC 2014

Source: [1-HKUST: Object Detection in ILSVRC 2014](<https://www.sota2.com/research/paper/68fa61841e209131ce9f4c9d>)

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

Data updated: 2025-10-25T20:01:28.850Z

Authors: Cewu Lu, Hao Chen, Qifeng Chen, Hei Law, Yao Xiao, Chi-Keung Tang

Published: 2014-09-22T12:42:08.000Z

## Abstract

The Imagenet Large Scale Visual Recognition Challenge (ILSVRC) is the one of the most important big data challenges to date. We participated in the object detection track of ILSVRC 2014 and received the fourth place among the 38 teams. We introduce in our object detection system a number of novel techniques in localization and recognition. For localization, initial candidate proposals are generated using selective search, and a novel bounding boxes regression method is used for better object localization. For recognition, to represent a candidate proposal, we adopt three features, namely, RCNN feature, IFV feature, and DPM feature. Given these features, category-specific combination functions are learned to improve the object recognition rate. In addition, object context in the form of background priors and object interaction priors are learned and applied in our system. Our ILSVRC 2014 results are reported alongside with the results of other participating teams.

## 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/1409.6155>)

[PDF](<https://arxiv.org/pdf/1409.6155.pdf>)