用冻结的SAMv2+轻量适配器，实现高效精准的显著物体检测。
Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection
- 用SAMv2做冻结编码器，搭配空间感知卷积适配器减少97%参数。
- 双解码器融合全局语义与局部细节，提升显著图精度。
- 适合资源有限但需高精度检测的场景，如移动端应用。
显著物体检测（SOD）在大规模视觉模型时代仍是一个重要但未充分探索的任务。尽管基础模型如SAM具备强大泛化能力，其在SOD中的潜力尚未完全释放，且训练或全量微调成本高昂，在数据有限时易过拟合。为此，我们提出GLASSNet，一种全局-局部特征解码框架，使用SAMv2作为冻结编码器，并引入轻量级、空间感知的卷积适配器，使可学习编码器参数减少超过97%。为提升显著性质量，GLASSNet采用双解码器架构：一个解码器通过扩大感受野捕获全局长程语义，另一个解码器专注于边缘、纹理等精细局部细节。融合互补线索生成兼具全局连贯性与局部精确性的显著图，最终输出准确掩码。在标准SOD和伪装物体检测基准上的大量实验表明，GLASSNet超越现有最优方法，证明了冻结基础模型结合定向适配与全局-局部解码的强大效能。
原文摘要 · Abstract (English)
Salient Object Detection (SOD) remains an essential yet underexplored task in the era of large-scale vision models. Although foundation models like SAM exhibit strong generalization, their potential for SOD is not fully realized, and training or fully fine-tuning them is computationally expensive and prone to overfitting under limited data. To overcome these challenges, we introduce GLASSNet, a Global-Local feature decoding framework that uses SAMv2 as a frozen encoder paired with a lightweight, spatially aware convolutional adapter-reducing learnable encoder parameters by over 97%. To enhance saliency quality, GLASSNet employs a dual-decoder architecture: one decoder captures global, long-range semantics with an expanded receptive field, while the other captures fine local details such as edges and textures. Fusing these complementary cues yields saliency maps that combine global coherence with local precision, producing accurate final masks. Extensive experiments on standard SOD and camouflaged object detection benchmarks show that GLASSNet surpasses state-of-the-art methods, demonstrating the power of frozen foundation models combined with targeted adaptation and global-local decoding.
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