no code implementations • 12 Jan 2025 • Du Chen, Liyi Chen, Zhengqiang Zhang, Lei Zhang
However, directly applying GS to ASR is exceptionally challenging because the original GS is an optimization-based method through overfitting each single scene, while in ASR we aim to learn a single model that can generalize to different images and scaling factors.
1 code implementation • 24 Oct 2024 • Zhengqiang Zhang, Ruihuang Li, Lei Zhang
While image generation with diffusion models has achieved a great success, generating images of higher resolution than the training size remains a challenging task due to the high computational cost.
1 code implementation • 19 Oct 2024 • Chaodong Xiao, Minghan Li, Zhengqiang Zhang, Deyu Meng, Lei Zhang
Selective state space models (SSMs), such as Mamba, highly excel at capturing long-range dependencies in 1D sequential data, while their applications to 2D vision tasks still face challenges.
1 code implementation • 11 Aug 2024 • Du Chen, Zhengqiang Zhang, Jie Liang, Lei Zhang
Based on the fact that natural images exhibit high self-similarities, i. e., a local patch can have many similar patches to it in the whole image, in this work we propose a simple yet effective self-similarity loss (SSL) to improve the performance of generative Real-ISR models, enhancing the hallucination of structural and textural details while reducing the unpleasant visual artifacts.
no code implementations • 13 Jul 2024 • Ruihuang Li, Zhengqiang Zhang, Chenhang He, Zhiyuan Ma, Vishal M. Patel, Lei Zhang
Recent vision-language pre-training models have exhibited remarkable generalization ability in zero-shot recognition tasks.
no code implementations • 25 Jun 2024 • Ruihuang Li, Liyi Chen, Zhengqiang Zhang, Varun Jampani, Vishal M. Patel, Lei Zhang
Meanwhile, the 2D diffusion models also exhibit substantial potentials for 3D editing tasks.
2 code implementations • 30 Dec 2023 • Lingchen Sun, Rongyuan Wu, Jie Liang, Zhengqiang Zhang, Hongwei Yong, Lei Zhang
Specifically, we propose a non-uniform timestep sampling strategy in the first stage.
1 code implementation • 25 Dec 2023 • Shi Guo, jianqi ma, Xi Yang, Zhengqiang Zhang, Lei Zhang
Extensive experiments demonstrate the leading VJDD performance of our method in term of restoration accuracy, perceptual quality and temporal consistency.
1 code implementation • 15 Dec 2023 • Zhengqiang Zhang, Ruihuang Li, Shi Guo, Yang Cao, Lei Zhang
Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information, while the strict latency requirement makes accurate and efficient alignment very challenging.
2 code implementations • CVPR 2024 • Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, Lei Zhang
Owe to the powerful generative priors, the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem.
1 code implementation • 24 Mar 2021 • Yang Cao, Zhengqiang Zhang, Enze Xie, Qibin Hou, Kai Zhao, Xiangui Luo, Jian Tuo
However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability.
no code implementations • 29 May 2019 • Zhengqiang Zhang, Shujian Yu, Shi Yin, Qinmu Peng, Xinge You
Weakly-supervised semantic segmentation aims to assign each pixel a semantic category under weak supervisions, such as image-level tags.
no code implementations • 21 Mar 2019 • Shi Yin, Zhengqiang Zhang, Qinmu Peng, Xinge You
High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice.
no code implementations • 5 Jan 2019 • Shi Yin, Zhengqiang Zhang, Hongming Li, Qinmu Peng, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
no code implementations • 12 Nov 2018 • Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.