Cost-Efficient Multi-Scale Fovea for Semantic-Based Visual Search Attention
Semantics are one of the primary sources of top-down preattentive information. Modern deep object detectors excel at extracting such valuable semantic cues from complex visual scenes. However, the size of the visual input to be processed by these detectors can become a bottleneck, particularly in terms of time costs, affecting an artificial attention system's biological plausibility and real-time deployability. Inspired by classical exponential density roll-off topologies, we apply a new artificial foveation module to our novel attention prediction pipeline: the Semantic-based Bayesian Attention (SemBA) framework. We aim at reducing detection-related computational costs without compromisi...
| Authors | João Luzio, Alexandre Bernardino, Plinio Moreno |
|---|---|
| Primary category | eess.IV |
| Published | 4 April 2026 |
| Last updated at source | 8 April 2026 |
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