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SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions

Paper recorded by Signals 4 on 2026-08-30 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.CV · 计算机视觉 · first seen 2026-09-01

Abstract

Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Snap-and-ask retrieval is now one of the most common entry points for mobile AI, yet photos are often blurry, while text questions may be short or mistyped. Existing benchmarks only test on clean inputs or do not isolate paired robustness in snap-and-ask retrieval. Therefore, we introduce Sn

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#210 most recent of 237 cs.CV papers we have recorded · ↑ newer: nnMNet: Baseline for Martian Terrain Semantic Segmentation · ↓ older: RePair: Turning Retrieval Failures into Counterfactual Hard Pairs
Cite this page: SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions: the #210 most recent of 237 cs.CV papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/snapbench-benchmarking-snap-and-ask-multimodal-retrieval-for-mobile-interactions.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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