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Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Key features-guided Multi-View Collaborative Network for image captioning.

Wencai Zhu¹, Zetao Jiang¹, Xu Wu²

¹Guangxi Key Lab of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 11, 2025

Summary

This summary is machine-generated.

This study introduces a Key features-guided Multi-view Collaborative Network (KMCN) to improve image captioning by reducing semantic noise. The novel approach enhances feature representation and cross-modal alignment for better performance.

Keywords:

Cross-guided dual-branch block Image captioning Key features-guided modeling Multi-view collaboration

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Area of Science:

Computer Vision
Artificial Intelligence
Natural Language Processing

Background:

Multi-view integration advances image captioning.
Semantic noise during integration hinders performance.

Purpose of the Study:

Propose a novel Key features-guided Multi-view Collaborative Network (KMCN).
Minimize semantic noise in image captioning.
Achieve complementary advantages from multi-view integration.

Main Methods:

Introduce Key features-guided Augmentation and Fusion Encoder (KAFE) for feature enhancement.
Utilize key features for complementary information and refined representation.
Implement Dual-branch Collaborative Decoder (DCD) for cross-modal semantic alignment.
Model inter-modal relationships via cross-guided dual-branch blocks.

Main Results:

KMCN effectively reduces semantic noise.
Refined multi-view feature representation is achieved.
Decomposition of complex feature spaces into simpler subspaces.
Outperforms state-of-the-art models on MS-COCO benchmark.

Conclusions:

KMCN offers a robust solution to semantic noise in multi-view image captioning.
The proposed KAFE and DCD modules are effective.
Demonstrates superior performance in both offline and online tests.