Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
EchoAlign adjusts instances with controllable generative models to match noisy labels and selects reliable subsets, outperforming prior methods on benchmarks especially under 30% instance-dependent noise.
citing papers explorer
-
From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
-
EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels
EchoAlign adjusts instances with controllable generative models to match noisy labels and selects reliable subsets, outperforming prior methods on benchmarks especially under 30% instance-dependent noise.