GramSR uses DINOv3 visual features instead of text captions to condition a one-step diffusion model for super-resolution via sequential pixel, semantic, and texture LoRA modules.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
RaTA-Tool retrieves suitable external tools for multimodal queries by matching generated task descriptions against tool metadata, supported by a new Hugging Face-derived dataset and DPO optimization.
citing papers explorer
-
GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution
GramSR uses DINOv3 visual features instead of text captions to condition a one-step diffusion model for super-resolution via sequential pixel, semantic, and texture LoRA modules.
-
RaTA-Tool: Retrieval-based Tool Selection with Multimodal Large Language Models
RaTA-Tool retrieves suitable external tools for multimodal queries by matching generated task descriptions against tool metadata, supported by a new Hugging Face-derived dataset and DPO optimization.