CLIP-driven hierarchical fusion for referring image segmentation

Yichen Yan; Xingjian He; Jing Liu

doi:10.1117/12.3033755

13 June 2024 CLIP-driven hierarchical fusion for referring image segmentation

Yichen Yan, Xingjian He, Jing Liu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800C (2024) https://doi.org/10.1117/12.3033755
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Referring image segmentation aims to segment an object mentioned in natural language from an image. It is a fundamental computer vision task. This task is challenging because it involves both vision and language features that need to be aligned and fused effectively. For alignment, pre-trained CLIP is widely used in many vision-language tasks for its notable success in aligning these two modalities. However, in the majority of existing methods, vision and language information are independent in the encoder stage, which is a suboptimal fusion approach. In this paper, we introduce an innovative CLIPDriven Hierarchical Fusion framework named CHRIS. We utilize CLIP as the encoder for its valuable vision-language alignment, we also design an effective early fusion approach in the encoder stage called hierarchical attention. Moreover, we introduce a novel hierarchical fusion neck to fuse vision and language information. In this way, the vision and language features contained in CLIP are further fused effectively. We perform comprehensive experiments on the three datasets widely adopted in the research community, RefCOCO, RefCOCO+, and G-Ref. Our proposed framework demonstrates superior performance compared to previous approaches by just using ResNet as the backbone.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yichen Yan, Xingjian He, and Jing Liu "CLIP-driven hierarchical fusion for referring image segmentation", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800C (13 June 2024); https://doi.org/10.1117/12.3033755

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Visualization

Image fusion

Feature fusion

Feature extraction

Information visualization

Visual process modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years