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SPOOK: Sparse Pixel Optimization for Off-Manifold Knowledge in Vision Encoders

Paper recorded by Signals 4 on 2026-10-23 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26

Category: cs.CL · 自然语言处理 · first seen 2026-10-26

Abstract

Modern vision encoders are widely used as semantic representations, but the relationship between embedding similarity and human-visible semantics can become unclear far from the natural-image manifold. We probe this regime in a black-box setting by applying sparse pixel edits to blank canvases with a simple pixel-level hill-climbing procedure. Across ImageNet and Flickr30k, we evaluate whether the

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#154 most recent of 660 cs.CL papers we have recorded · ↑ newer: Communicating with the Entire Population: Machine Translation of Emerg · ↓ older: KrishokTech: A Bengali Agricultural Advisory System with Explicit Auth
Cite this page: SPOOK: Sparse Pixel Optimization for Off-Manifold Knowledge in Vision Encoders: the #154 most recent of 660 cs.CL papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/spook-sparse-pixel-optimization-for-off-manifold-knowledge-in-vision-encoders.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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