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arXiv Computer Vision By Shivanshu Shekhar, Raghavendra Addanki, Tong Zhang, Uttaran Bhattacharya

Spend Where It Matters: Particle Search with MH Refinement for Budget-Constrained Audio-Visual Alignment

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The paper introduces “Spend Where It Matters”, a two‑stage, training‑free framework that combines particle search with Metropolis‑Hastings refinement for budget‑constrained audio‑visual alignment. In the first stage, particles are adaptively allocated across denoising checkpoints based on online variance estimates, reducing estimation variance while focusing compute where it matters most. The second stage uses MH refinement with local noise‑and‑denoise proposals to further improve reward, starting from the strong candidate produced by the particle search. Experiments on “JavisBench‑Mini” show that this combined approach yields higher rewards than either particle search or MH alone, achieving significant relative gains over baseline methods. The method also demonstrates benefits for image generation on SD1.5, improving aesthetic and image reward scores compared to uniform particle allocation.

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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