QuADA-GS learns to predict local complexity-driven Gaussian densification from low-resolution inputs and uses Hierarchical Pointer Convolution for efficient arbitrary-scale super-resolution.
Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3representative citing papers
LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
A ray-tracing pipeline aligns ground-level image pixels to outdated DEM rasters for real-time 3D terrain reconstruction in wildfire zones, validated primarily through a custom simulator.
citing papers explorer
-
Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
QuADA-GS learns to predict local complexity-driven Gaussian densification from low-resolution inputs and uses Hierarchical Pointer Convolution for efficient arbitrary-scale super-resolution.
-
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
-
LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
A ray-tracing pipeline aligns ground-level image pixels to outdated DEM rasters for real-time 3D terrain reconstruction in wildfire zones, validated primarily through a custom simulator.