4 papers
EVA: Bridging Performance and Human Alignment in Hard-Attention Vision Models for Image Classification
Pengcheng Pan, Yonekura Shogo, Kuniyoshi Yasuo
Optimizing vision models purely for classification accuracy can impose an alignment tax, degrading human-like scanpaths and limiting interpretability. We introduce EVA, a neuroscie…
Debiasing Central Fixation Confounds Reveals a Peripheral "Sweet Spot" for Human-like Scanpaths in Hard-Attention Vision
Pengcheng Pan, Yonekura Shogo, Yasuo Kuniyosh
Human eye movements in visual recognition reflect a balance between foveal sampling and peripheral context. Task-driven hard-attention models for vision are often evaluated by how…
Synaptic bundle theory for spike-driven sensor-motor system: More than eight independent synaptic bundles collapse reward-STDP learning
Takeshi Kobayashi, Shogo Yonekura, Yasuo Kuniyoshi
Neuronal spikes directly drive muscles and endow animals with agile movements, but applying the spike-based control signals to actuators in artificial sensor-motor systems inevitab…
Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision
Pengcheng Pan, Yonekura Shogo, Yasuo Kuniyoshi
Inspired by foveal vision, hard attention models promise interpretability and parameter economy. However, existing models like the Recurrent Model of Visual Attention (RAM) and Dee…