51 citations · 166 across the 18 of their papers we have counts for
13 papers · 1 filter
MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
Shuzhao Xie, Junchen Ge, Weixiang Zhang +10
3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical deployment. Existing post-t…
Soundini: Sound-Guided Diffusion for Natural Video Editing
Seung Hyun Lee, Sieun Kim, Innfarn Yoo +6
We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting. Animating the appearance of the visual effect is challenging beca…
MRET: Multi-resolution Transformer for Video Quality Assessment
Junjie Ke, Tianhao Zhang, Yilin Wang +2
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA ta…
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
Junjie Ke, Keren Ye, Jiahui Yu +3
Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic…
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
Ligong Han, Yinxiao Li, Han Zhang +3
Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities. However, existing…
MaxViT: Multi-Axis Vision Transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang +4
Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size h…
MUSIQ: Multi-scale Image Quality Transformer
Junjie Ke, Qifei Wang, Yilin Wang +2
Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional…
Adversarially Adaptive Normalization for Single Domain Generalization
Xinjie Fan, Qifei Wang, Junjie Ke +3
Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversaria…
COMISR: Compression-Informed Video Super-Resolution
Yinxiao Li, Pengchong Jin, Feng Yang +3
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression. However, most videos on the…
Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
Innfarn Yoo, Huiwen Chang, Xiyang Luo +4
Digital watermarking is widely used for copyright protection. Traditional 3D watermarking approaches or commercial software are typically designed to embed messages into 3D meshes,…
Deep Perceptual Image Quality Assessment for Compression
Juan Carlos Mier, Eddie Huang, Hossein Talebi +2
Lossy Image compression is necessary for efficient storage and transfer of data. Typically the trade-off between bit-rate and quality determines the optimal compression level. This…
Multi-path Neural Networks for On-device Multi-domain Visual Classification
Qifei Wang, Junjie Ke, Joshua Greaves +9
Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constra…
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
Xiang Zhu, Hossein Talebi, Xinwei Shi +2
In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution meth…