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arXiv Computer Vision By Johann Schmidt, Sebastian Stober, Joachim Denzler, Paul Bodesheim

PPS: Plug-and-Play Saccadic Vision for Fine-Grained Classification

Read the original on arXiv Computer Vision →

PPS: Plug-and-Play Saccadic Vision for Fine-Grained Classification proposes a lightweight extension that transforms any pretrained backbone with spatial feature maps into a saccadic classifier. The method adds only a small priority head and a single cross-attention fusion block, using a weight-tied encoder to process a downsampled peripheral glance and high-resolution glimpse crops selected by a priority distribution. A sequential sampler with non-maximum suppression draws a configurable number of glimpses, and a residual multi-head cross-attention block fuses the glance with the glimpses; the priority head is trained via a per-glimpse information-gain target from a lightweight memory bank, avoiding reinforcement learning. The approach achieves linear complexity in the number of glimpses and decouples peak memory from input resolution, consistently improving performance across nine fine-grained classification benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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