English(EN) Multi-Scale Fruit Capsules: Dilated Convolutions and Dynamic Routing for In-the-Wild Explainable Fruit Recognition

FruitCapsNet 使用扩张卷积进行可解释水果识别

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了 FruitCapsNet，这是一种新颖的胶囊网络，专为在各种真实世界条件下进行可解释的水果识别而设计。该网络在其胶囊内利用扩张卷积来捕获多尺度上下文信息，然后采用动态路由来解决空间关系。在包括新的野外数据集在内的多个数据集上进行的实验表明，FruitCapsNet 的性能优于十个经过微调的迁移学习骨干网络，在最具挑战性的数据集上取得了最大的收益。Grad-CAM显著性图增强了系统的可解释性，将决策归因于整个水果区域，从而证明性能的提高并非数据集的伪影。 AI

影响引入了一种新颖的架构，可提高计算机视觉任务的可解释性和性能。

排序理由该集群包含一篇详细介绍新模型及其实验结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Subhankar Chattoraj, Sawon Pratiher, Samiran Das, Hubert Konik · 2026-08-25 04:00

多尺度水果胶囊：用于野外可解释水果识别的膨胀卷积和动态路由

arXiv:2608.21454v1 Announce Type: new Abstract: The same fruit appears in a bunch, unpicked, peeled, bagged in plastic, or sliced on a dish, so automated fruit classification in the wild (AFCW) must absorb wide intra- class and narrow inter-class variability in shape, size, colou…

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多尺度水果胶囊：用于野外可解释水果识别的膨胀卷积和动态路由

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