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Updated: Jun 29, 2026

Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice

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Published on: August 23, 2022

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear

Siyan Sun¹, Peng Wang¹, Hong Peng¹

¹School of Computer and Software Engineering, Xihua University, 999 Jinzhou Road, Chengdu, Sichuan, P. R. China.

International Journal of Neural Systems

|September 23, 2024

Summary

This summary is machine-generated.

This study introduces NSNPRIS, a new model for referring image segmentation that aligns visual and text features. NSNPRIS improves segmentation accuracy by enhancing feature interaction and alignment.

Keywords:

Convolutional nonlinear spiking neural P systems feature alignment feature interaction referring image segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Computational Neuroscience

Background:

Referring image segmentation requires precise alignment of image pixels with textual descriptions for object identification.
Existing methods face challenges in effectively integrating multi-modal features for accurate segmentation.

Purpose of the Study:

To introduce NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model for enhanced referring image segmentation.
To improve the alignment between pixel and textual features for superior segmentation accuracy.

Main Methods:

NSNPRIS utilizes convolutional nonlinear spiking neural P systems.
Key components include NSNPFusion and Language Gate modules for improved feature interaction during encoding.
An NSNPDecoder facilitates feature alignment and decoding.

Main Results:

NSNPRIS demonstrated superior performance compared to mainstream methods on RefCOCO, RefCOCO[Formula: see text], and G-Ref datasets.
The model effectively advanced the alignment of pixel and textual features.
Significant improvements in segmentation accuracy were achieved.

Conclusions:

NSNPRIS offers a novel and effective approach to referring image segmentation.
The proposed model advances the state-of-the-art in aligning visual and linguistic information for object segmentation.
The architecture enhances feature interaction and alignment, leading to improved segmentation outcomes.