MFEAM: Multi-View Feature Enhanced Attention Model for Image Captioning
Image captioning plays a crucial role in aligning visual content with natural language, serving as a key step toward effective cross-modal understanding. Transformer has become the dominant language model in image captioning. Existing Transformer-based models seldom highlight important features from...
Saved in:
| Main Authors: | Yang Cui, Juan Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/15/8368 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Chinese Image Captioning Based on Deep Fusion Feature and Multi-Layer Feature Filtering Block
by: Xi Yang, et al.
Published: (2025-01-01) -
Enhanced group relation learning via aligned attention masking for fashion product captioning
by: Yuhao Tang, et al.
Published: (2025-08-01) -
Enabling High-Level Worker-Centric Semantic Understanding of Onsite Images Using Visual Language Models with Attention Mechanism and Beam Search Strategy
by: Hui Deng, et al.
Published: (2025-03-01) -
A novel image captioning model with visual-semantic similarities and visual representations re-weighting
by: Alaa Thobhani, et al.
Published: (2024-09-01) -
AFNE-Net: Semantic Segmentation of Remote Sensing Images via Attention-Based Feature Fusion and Neighborhood Feature Enhancement
by: Ke Li, et al.
Published: (2025-07-01)