TL;DR
The Local Dilated Convolution module and its lightweight variant, Ghost-LDC are introduced and it is shown that LDC-based modules can improve performance in some tested dense prediction settings, while the semantic segmentation results are more configuration-sensitive.
A global-local fusion segmentation model named global-local bubble network is designed, which uses the visual state space encoder as the backbone for feature extraction and establishes long-range pixel dependencies, significantly enhancing context inference capabilities under dense occlusion.
Experiments show that EDC outperforms state-of-the-art deformable convolution variants, including Deformable ConvNets V1-V4 and Entire Deformable ConvNets, across mainstream segmentation datasets with various decoder settings, and ablation studies confirm the effectiveness of each component.
The proposed WMW-SAM, a Weighted Multi-Scale and Wavelet-Enhanced SAM, is designed to handle limitations of existing adaptation strategies for SOD, and develops the Weighted Multi-Scale Adapter (WMSA), which utilizes learnable weighting across multiple receptive fields to calibrate features of different scales.
Accurate retinal vessel segmentation is an important foundation for assisted screening and quantitative analysis of ophthalmic diseases and systemic diseases. However, the edge and high-frequency responses of thin, low-contrast vessels are easily attenuated during successive convolution operations and multilevel downsa...
The core innovation lies in a novel elastic pixel differential convolution operator, which flexibly captures microscopic gradient cues to effectively coordinate high-level semantics with low-level details during inference with zero additional computational overhead.
The Regional Self-Attention Mechanism is proposed, which strictly confines the self-attention operation to the independent regions defined by the target bounding boxes, enabling the model to generate specific-category targets in a targeted manner during the denoising process.
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