Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of scene complexity or task relevance. Inspired by biological vision, we investigate whether semantic understanding can be achieved more efficiently through digital foveated perception. We introduce a lightweight active-vision pipeline that combines saliency-driven fixation selection, high-resolution foveal observations, low-resolution contextual information, semantic accumulation, and adaptive computation. Beyond conventional dense prediction metrics, we use object-level evaluation to measure semantic understanding under sparse observations. On ADE20K-Object, a single foveated observation achieves 95.9% of the baseline Top-1 accuracy and 96.9% of the baseline Top-3 accuracy while requiring only 4.7% of the computational cost. At the scene level, semantic accumulation recovers 90.6% of the baseline object recall while using 58.6% of the computation. These results suggest that substantial semantic understanding can emerge from sparse observations when computation is allocated selectively, highlighting active vision as an efficient alternative to uniform dense processing and motivating evaluation protocols beyond conventional pixel-wise segmentation metrics.
Caterina Caccavella, Vittorio Fra, Andreas Ziegler et al.· 0 citations
Deploying anomaly detection models on energy constrained industrial platforms is an open and pivotal challenge for manufacturing. This paper presents ALPS (Anomaly detection with Low Power Spiking neural networks), a lightweight neuro-morphic pipeline for acoustic fault classification in a reciprocating air compressor. Raw audio waveforms are decomposed by a 16-channel band pass filter bank and converted into spike trains, which are then processed by a feed-forward spiking neural network with one hidden layer composed of 128 leaky integrate-and-fire neurons. The full pipeline is deployed on the SynSense Xylo Audio 3 neuromorphic processor after 7-bit post-training quantization. On an eight-class benchmark dataset, the system achieves 0.93 macro F1 while consuming only 3.4 mW, roughly three orders of magnitude less than a 1D convolutional neural network running on a Raspberry Pi 4 at comparable accuracy. A robustness study with structured additive factory noise shows graceful performance degradation and no abrupt collapse. These results demonstrate that neuromorphic hardware is a viable, ultra-low-power alternative for always-on acoustic anomaly detection at the edge.
F. Aisa, Umberto Albertin, Mauro Martini et al.· 2026 IEEE/ASME International...· 0 citations
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