Muyao Niu

ORCID: 0000-0002-4613-9636
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About
Contact & Profiles
Research Areas
  • Generative Adversarial Networks and Image Synthesis
  • Image Enhancement Techniques
  • Video Analysis and Summarization
  • Text and Document Classification Technologies
  • Topic Modeling
  • Human Mobility and Location-Based Analysis
  • Advanced Vision and Imaging
  • Data Management and Algorithms
  • Human Pose and Action Recognition
  • Geographic Information Systems Studies
  • Web Data Mining and Analysis

The University of Tokyo
2023

Dalian University of Technology
2021-2023

Automatic sketch colorization is a challenging task that aims to generate color image from sketch, primarily due its inherently ill-posed nature. While many approaches have shown promising results, two significant challenges remain: limited patterns and wide range of artifacts such as bleeding semantic inconsistencies among relevant regions. These issues stem the operation traditional convolutional structures, which capture structural features in pixel-wise manner, resulting inadequate...

10.1109/tip.2023.3326682 article EN IEEE Transactions on Image Processing 2023-01-01

Spatio-temporal compression of videos, utilizing networks such as Variational Autoencoders (VAE), plays a crucial role in OpenAI's SORA and numerous other video generative models. For instance, many LLM-like models learn the distribution discrete tokens derived from 3D VAEs within VQVAE framework, while most diffusion-based capture continuous latent extracted by 2D without quantization. The temporal is simply realized uniform frame sampling which results unsmooth motion between consecutive...

10.48550/arxiv.2405.20279 preprint EN arXiv (Cornell University) 2024-05-30

Muyao Niu, Jie Cai. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint (EMNLP-IJCNLP). 2019.

10.18653/v1/d19-1344 article EN cc-by 2019-01-01

In our daily life, we prefer to describe the location of a person with semantic words such as "travel," "cinema" rather than geographic terminology GPS coordinates ($32^{\circ }24^{^{\prime }}12^{^{\prime \prime }}$32∘24'12''N, $2^{\circ }90^{^{\prime }}26.5^{^{\prime }}$2∘90'26.5''E). This is because places are easier understand compared precise location. Existing methods only take text-image pairs into consideration, neglecting user attribute, which essential in place prediction. this...

10.1109/mmul.2021.3089719 article EN IEEE Multimedia 2021-06-16
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