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Related Experiment Video

Updated: May 30, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Quantifying and transferring contextual information in object detection.

Wei-Shi Zheng¹, Shaogang Gong, Tao Xiang

¹School of Information Science and Technology, Sun Yat-sen University, Guangzhou, Guangdong 510006, P.R. China. wszheng@ieee.org

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 17, 2011

Summary

This summary is machine-generated.

This study introduces a new framework for object detection that quantifies and transfers contextual information, improving accuracy even with limited training data. The maximum margin context (MMC) model effectively selects relevant context for better object recognition.

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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

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Published on: December 15, 2023

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Last Updated: May 30, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Object detection accuracy is often limited by contextual understanding.
Modeling diverse contextual information is challenging due to varying relevance and data requirements.
Existing methods struggle with limited training data and require scene annotations.

Purpose of the Study:

To develop a novel context modeling framework for object detection.
To automatically quantify and select effective contextual information.
To address context learning challenges with limited data using transfer learning.

Main Methods:

Formulated a polar geometric context descriptor for multi-type contextual information.
Proposed a maximum margin context (MMC) model for context quantification via discriminant inference.
Developed two context transfer learning models using joint maximum margin learning for data-scarce scenarios.

Main Results:

Validated the effectiveness of the proposed models on PASCAL VOC, luggage, and vehicle detection datasets.
Demonstrated superior performance compared to alternative context models.
Showcased successful quantification and transfer of contextual information.

Conclusions:

The proposed framework effectively quantifies and transfers contextual information for object detection.
The MMC model and transfer learning approaches significantly improve detection accuracy, especially with limited data.
This work offers a robust solution for context-aware object detection without prior scene segmentation or annotation.