RGBT segmentation is a challenging task in the area of computer vision. Current advanced networks for RGBT segmentation focus on extracting deeper discriminative features from RGB and thermal images to provide richer semantic information for the fusion features to the decoder. However, excessively mining deeper semanti...
Shao-Chuan Zhao, Chi Zhang, Han-Cheng Zhu et al.· Applied Sciences· 0 citations
The emergence of large-scale vision–language models (VLMs) has significantly advanced remote sensing image–text retrieval (RSITR) by providing powerful cross-modal semantic priors. However, when adapted to the remote sensing (RS) domain, these models struggle to capture fine-grained representations due to their inheren...
Wen-Liang Du, Xiao-Yu Xu, Jia-Qi Zhao et al.· IEEE Transactions on Geoscie...· 0 citations
Existing class-incremental learning (CIL) methods for remote sensing (RS) scene classification often tend to be training-intensive or rely on static visual features that may inadequately capture the complex interclass similarity and intraclass diversity inherent in RS imagery. Moreover, directly reusing features from m...
Wen-Liang Du, Ji-Cun He, Jia-Qi Zhao et al.· IEEE Transactions on Geoscie...· 0 citations
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