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Related Experiment Video

Updated: Jan 2, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Plenty is Plague: Fine-Grained Learning for Visual Question Answering.

Yiyi Zhou, Rongrong Ji, Xiaoshuai Sun

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 5, 2019

Summary

Training Visual Question Answering (VQA) models is costly. This study introduces FG-A1C, a fine-grained learning paradigm that intelligently schedules data, reducing training costs and improving accuracy by addressing difficulty diversity and label redundancy.

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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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Last Updated: Jan 2, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

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

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Visual Question Answering (VQA) models face significant training costs due to large datasets and complex architectures.
Existing VQA training paradigms randomly sample data, overlooking crucial issues like "difficulty diversity" and "label redundancy".

Purpose of the Study:

To propose a fine-grained learning paradigm, FG-A1C, that enhances VQA training efficiency and model accuracy.
To address the challenges of varying question difficulty and redundant/noisy labels in VQA datasets.

Main Methods:

Introduced FG-A1C, an actor-critic based learning agent that adaptively schedules difficult question types.
Implemented two curriculum learning schemes to identify and learn the most useful data within question types.
Validated the approach on VQA2.0 and VQA-CP v2 datasets.

Main Results:

FG-A1C significantly improves training efficiency and model accuracy.
On VQA-CP v2, the method achieved better performance using less than 75% of the training data compared to using the full dataset.
Demonstrated effectiveness in guiding data labeling and seamless integration with existing VQA models.

Conclusions:

Fine-grained learning paradigms, like FG-A1C, offer substantial benefits for VQA training.
Intelligent data scheduling and curriculum learning effectively tackle training cost and accuracy challenges.
The proposed method is versatile and can be integrated with various VQA architectures without structural modifications.