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Enhancing Query Formulation for Universal Image Segmentation.

Yipeng Qu¹, Joohee Kim¹

¹Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

Efficient Query Optimizer (EQO) enhances image segmentation by reducing computational complexity. This novel approach improves performance over existing methods like OneFormer, offering more robust object query representations.

Keywords:

computer vision image segmentation panoptic segmentation semantic segmentation transformer

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Vision Transformers have driven advancements in image segmentation.
Existing models like OneFormer face challenges with computational demands and inefficiencies in text generation and contrastive loss computation.

Purpose of the Study:

To introduce an efficient approach for query optimization in image segmentation.
To reduce computational complexity and improve performance in transformer-based segmentation models.

Main Methods:

Developed Efficient Query Optimizer (EQO) utilizing multi-modal data for query refinement.
Implemented a strategy to distill image information into a single template sentence, reducing parameters and computations.
Proposed a novel attention-based contrastive loss for a one-to-many matching mechanism to enhance object query representations.

Main Results:

EQO significantly reduces complexity compared to OneFormer.
The model demonstrates superior performance across three segmentation tasks using the Swin-T backbone.
Outperformed OneFormer on the ADE20K dataset by 0.2% in mIoU, 0.6% in AP, and 0.8% in PQ.

Conclusions:

EQO effectively addresses inefficiencies in transformer-based image segmentation.
The proposed methods lead to more robust object query learning and improved segmentation accuracy.
EQO represents a significant advancement in efficient and high-performance image segmentation.