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Related Experiment Video

Updated: Oct 4, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Siamese Implicit Region Proposal Network With Compound Attention for Visual Tracking.

Sixian Chan, Jian Tao, Xiaolong Zhou

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel Siamese implicit region proposal network with compound attention for enhanced visual tracking. The new method improves feature representation and achieves state-of-the-art performance on multiple benchmarks.

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Area of Science:

Computer Vision
Machine Learning

Background:

Siamese-based trackers show success but struggle with consistent object feature representation.
Challenges include learning robust features for accurate object tracking.

Purpose of the Study:

To propose a novel Siamese implicit region proposal network with compound attention for visual tracking.
To improve the learning of consistent object feature representations.

Main Methods:

Designed an implicit region proposal (IRP) module using pixel-wise correlation to aggregate similar feature information.
Developed a compound attention module (channel and non-local attention) to enhance object scale and shape perception.
Adaptive feature receptive fields are obtained via linear fusion of features.

Main Results:

The proposed tracker achieved state-of-the-art performance on six challenging benchmark datasets (VOT-2018, VOT-2019, OTB-100, GOT-10k, LaSOT, TrackingNet).
The tracker operates at a real-time speed of 72 FPS.

Conclusions:

The novel Siamese implicit region proposal network with compound attention effectively addresses challenges in visual tracking.
The approach demonstrates superior performance and real-time applicability.