Referring Image Segmentation With Fine-Grained Semantic Funneling Infusion

Area of Science:

• Computer Vision
• Natural Language Processing
• Artificial Intelligence

Background:

• Referring image segmentation is crucial for human-robot interaction.
• Existing methods struggle with coarse fusion or high computational costs.
• Integrating language and image semantics effectively remains a challenge.

Purpose of the Study:

• To propose a novel mechanism for fine-grained semantic infusion in referring image segmentation.
• To address the issue of detail blurring caused by high-level semantic integration.
• To improve the overall performance and accuracy of referring image segmentation models.

Main Methods:

• Introduced a Fine-Grained Semantic Funneling Infusion (FSFI) mechanism with spatial constraints.
• Decomposed features into multiple low-dimensional spaces for effective multi-modal fusion.
• Developed a Multiscale Attention-Enhanced Decoder (MAED) with a detail enhancement operator (DeEh).

Main Results:

• The proposed FSFI mechanism enables more effective and representative information fusion.
• The MAED, utilizing attention guidance, successfully preserves and enhances referent details.
• The network demonstrated superior performance compared to state-of-the-art methods on challenging benchmarks.

Conclusions:

• The developed FSFI and MAED mechanisms significantly advance referring image segmentation.
• The approach offers a more robust solution for understanding referred regions in images.
• This work contributes to more sophisticated human-robot interaction through improved visual-linguistic understanding.