Search Results for author:
Found 13 papers, 7 papers with code
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation
We then present a new I2V model, named MotionStone, developed with the decoupled motion estimator.
Mimir: Improving Video Diffusion Models for Precise Text Understanding
Text serves as the key control signal in video generation due to its narrative nature.
Machine learning topological energy braiding of non-Bloch bands
Machine learning has been used to identify phase transitions in a variety of physical systems.
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance
Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in over-smoothed content, structural distortions, and repetitive patterns.
Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment
Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information.
Mitigating Artifacts in Real-World Video Super-Resolution Models
To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated.
Rethinking Alignment in Video Super-Resolution Transformers
In this paper, we rethink the role of alignment in VSR Transformers and make several counter-intuitive observations.
Ranked #6 on
Video Super-Resolution
on Vid4 - 4x upscaling
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network
Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality.
Ranked #1 on
Image Quality Assessment
on MSU FR VQA Database
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception.
Ranked #8 on
Video Quality Assessment
on MSU SR-QA Dataset
Accelerating Neural Network Optimization Through an Automated Control Theory Lens
We conduct experiments both from a control theory lens through a phase locus verification and from a network training lens on several models, including CNNs, Transformers, MLPs, and on benchmark datasets.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Region-Adaptive Deformable Network for Image Quality Assessment
Image quality assessment (IQA) aims to assess the perceptual quality of images.
