Author

Seongjin Choi

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Jul 2026

Visual Distribution Anchoring for Efficient Prompt Tuning

This work proposes VDA (Visual Distribution Anchoring), a training-free target adaptation framework that augments a frozen semantic classifier with class-level visual prototypes estimated offline from an unlabeled target pool, and shows that class-specific partitioning drives gains and that visually local pseudo-label errors can remain useful despite being class-incorrect.

Pouya Parsa, Raoof Zare Moayedi, Seongjin Choi · arXiv.org · 0 citations

Preprint Jul 2026

Can the Cloud Drive? Infrastructure Feasibility of Offloading Autonomous Driving Across 5G and 6G

An analytical framework coupling communication limits, a roofline GPU service model, stochastic latency, and utilization-aware cost across three model classes, three offloading strategies, and three communication generations is applied to New York City.

Pouya Parsa, Kawon Han, Seongjin Choi · 0 citations