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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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TOSQ: Transparent Object Segmentation via Query-Based Dictionary Lookup with Transformers.

Bin Ma¹, Ming Ma¹, Ruiguang Li²

¹College of Electronic and Information Engineering, Hubei University of Automatic Technology, No. 167 Checheng West Road, Shiyan 442002, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces Transparent Object Segmentation through Query (TOSQ), a novel transformer-based model for accurately segmenting transparent objects. TOSQ significantly improves performance on challenging transparent object segmentation tasks.

Keywords:

deeping learning semantic segmentation semantic segmentation of transparent objects transformer

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Transparent object segmentation is crucial for applications like robot navigation and grasping.
Challenges include unpredictable scenes, lack of fixed visual patterns, and background interference.

Purpose of the Study:

To address the difficulties in transparent object segmentation using transformer architectures.
To develop a high-performance, end-to-end model for accurate transparent object segmentation.

Main Methods:

Leveraging transformer architectures for global modeling capabilities.
Introducing a Query Parsing Module (QPM) that treats segmentation as a dictionary lookup problem.
Developing the Transparent Object Segmentation through Query (TOSQ) model with a Segformer backbone and QPM-based decoder.

Main Results:

TOSQ achieved state-of-the-art performance on the Trans10K-V2 dataset with 76.63% mIoU and 95.34% Acc.
Demonstrated significant improvements in segmenting challenging categories like windows (+23.59%) and glass doors (+11.22%).

Conclusions:

The proposed TOSQ model effectively overcomes challenges in transparent object segmentation.
TOSQ's innovative QPM approach offers a superior alternative to conventional pixel-wise segmentation methods.