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arXiv Computer Vision By Hossein Shahabadi, Niki Sepasian, Mahdieh Soleymani Baghshah

VISTA: Test-Time Compositional Alignment for Visual Autoregressive Generation

Read the original on arXiv Computer Vision →

VISTA is a gradient‑based test‑time alignment framework designed for next‑scale visual autoregressive (VAR) image generation. It optimizes intermediate representations within the frozen transformer to enforce compositional constraints, without altering model weights or requiring extra training. Experiments on two benchmarks and two model scales show that VISTA improves compositional accuracy by up to 20% on a 2B backbone and 6% on an 8B backbone, while preserving image quality and enabling a smaller model to outperform a larger one.

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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