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Related Experiment Video

Updated: Jul 3, 2025

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

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Progressive Frame-Proposal Mining for Weakly Supervised Video Object Detection.

Mingfei Han, Yali Wang, Mingjie Li

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

|February 15, 2024

Summary

This summary is machine-generated.

This study introduces a Progressive Frame-Proposal Mining (PFPM) framework to improve weakly supervised video object detection. PFPM effectively mines object proposals from videos using only tags, outperforming existing methods.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Weakly supervised video object detection trains models using only object labels, lacking precise bounding box data.
Existing methods struggle with redundant frames and ineffective proposal mining in weakly annotated videos.

Purpose of the Study:

To develop an effective framework for weakly supervised video object detection.
To improve the accuracy and efficiency of object detection in videos with limited annotations.

Main Methods:

Propose a Progressive Frame-Proposal Mining (PFPM) framework.
Implement a Multi-Level Selection (MLS) scheme guided by video tags to select relevant frames and mine proposals.
Introduce a Holistic-View Refinement (HVR) scheme for self-supervised refinement of pseudo ground truth boxes.

Main Results:

The PFPM framework significantly enhances weakly supervised object detection performance.
The MLS scheme effectively reduces frame redundancy and improves proposal quality.
The HVR scheme accurately refines bounding boxes for improved training.

Conclusions:

The proposed PFPM framework offers a robust solution for weakly supervised video object detection.
PFPM demonstrates superior performance compared to state-of-the-art methods on the ImageNet VID benchmark.
The framework's ability to leverage coarse annotations marks a significant advancement in the field.