MatRes jointly optimizes restoration and correspondence estimation at test time by enforcing conditional similarity on a single image pair and adapting lightweight modules without offline training.
arXiv preprint arXiv:2306.03881 (2023) 14
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2representative citing papers
BLINK benchmark shows multimodal LLMs reach only 45-51 percent accuracy on core visual perception tasks where humans achieve 95 percent, indicating these abilities have not emerged.
citing papers explorer
-
MatRes: Zero-Shot Test-Time Model Adaptation for Simultaneous Matching and Restoration
MatRes jointly optimizes restoration and correspondence estimation at test time by enforcing conditional similarity on a single image pair and adapting lightweight modules without offline training.
-
BLINK: Multimodal Large Language Models Can See but Not Perceive
BLINK benchmark shows multimodal LLMs reach only 45-51 percent accuracy on core visual perception tasks where humans achieve 95 percent, indicating these abilities have not emerged.