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CADFormer: Fine-Grained Cross-modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation by Liu, Maofu; Jiang, Xin; Zhang, Xiaokang is a scholarly article available to read on EtoBox.

What is CADFormer: Fine-Grained Cross-modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation about?

Referring Remote Sensing Image Segmentation (RRSIS) is a challenging task, aiming to segment specific target objects in remote sensing (RS) images based on a given language expression. Existing RRSIS methods typically employ coarse-grained unidirectional alignment approaches to obtain multimodal features, and they often overlook the critical role of language features as contextual information during the decoding process. Consequently, these methods exhibit weak object-level correspondence between visual and language features, leading to incomplete or erroneous predicted masks, especially when handling complex expressions and intricate RS image scenes. To address these challenges, we propose a fine-grained cross-modal alignment and decoding Transformer, CADFormer, for RRSIS. Specifically, we design a semantic mutual guidance alignment module (SMGAM) to achieve both vision-to-language and language-to-vision alignment, enabling comprehensive integration of visual and textual features for fine-grained cross-modal alignment. Furthermore, a textual-enhanced cross-modal decoder (TCMD) is introduced to incorporate language features during decoding, using refined textual information as cont

Author

Liu, Maofu; Jiang, Xin; Zhang, Xiaokang

Published

2025

Language

EN