CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image
Segmentation
- URL: http://arxiv.org/abs/2305.11481v3
- Date: Wed, 14 Feb 2024 15:41:53 GMT
- Title: CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image
Segmentation
- Authors: Wenxuan Wang, Jing Liu, Xingjian He, Yisi Zhang, Chen Chen, Jiachen
Shen, Yan Zhang, Jiangyun Li
- Abstract summary: We propose a novel cross-modality masked self-distillation framework named CM-MaskSD.
Our method inherits the transferred knowledge of image-text semantic alignment from CLIP model to realize fine-grained patch-word feature alignment.
Our framework can considerably boost model performance in a nearly parameter-free manner.
- Score: 29.885991324519463
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Referring image segmentation (RIS) is a fundamental vision-language task that
intends to segment a desired object from an image based on a given natural
language expression. Due to the essentially distinct data properties between
image and text, most of existing methods either introduce complex designs
towards fine-grained vision-language alignment or lack required dense
alignment, resulting in scalability issues or mis-segmentation problems such as
over- or under-segmentation. To achieve effective and efficient fine-grained
feature alignment in the RIS task, we explore the potential of masked
multimodal modeling coupled with self-distillation and propose a novel
cross-modality masked self-distillation framework named CM-MaskSD, in which our
method inherits the transferred knowledge of image-text semantic alignment from
CLIP model to realize fine-grained patch-word feature alignment for better
segmentation accuracy. Moreover, our CM-MaskSD framework can considerably boost
model performance in a nearly parameter-free manner, since it shares weights
between the main segmentation branch and the introduced masked
self-distillation branches, and solely introduces negligible parameters for
coordinating the multimodal features. Comprehensive experiments on three
benchmark datasets (i.e. RefCOCO, RefCOCO+, G-Ref) for the RIS task
convincingly demonstrate the superiority of our proposed framework over
previous state-of-the-art methods.
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