Zobrazeno 1 - 10
of 320
pro vyhledávání: '"Lyu, Siwei"'
Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed. To achieve both efficient and high-quality synthesis, various distillation-based accelerated sam
Externí odkaz:
http://arxiv.org/abs/2409.19681
Autor:
Zhan, Zheyuan, Chen, Defang, Mei, Jian-Ping, Zhao, Zhenghe, Chen, Jiawei, Chen, Chun, Lyu, Siwei, Wang, Can
Conditional image synthesis based on user-specified requirements is a key component in creating complex visual content. In recent years, diffusion-based generative modeling has become a highly effective way for conditional image synthesis, leading to
Externí odkaz:
http://arxiv.org/abs/2409.19365
In recent years, the multimedia forensics and security community has seen remarkable progress in multitask learning for DeepFake (i.e., face forgery) detection. The prevailing strategy has been to frame DeepFake detection as a binary classification p
Externí odkaz:
http://arxiv.org/abs/2408.16305
Recent research on knowledge distillation has increasingly focused on logit distillation because of its simplicity, effectiveness, and versatility in model compression. In this paper, we introduce Refined Logit Distillation (RLD) to address the limit
Externí odkaz:
http://arxiv.org/abs/2408.07703
The CNN has achieved excellent results in the automatic classification of medical images. In this study, we propose a novel deep residual 3D attention non-local network (NL-RAN) to classify CT images included COVID-19, common pneumonia, and normal to
Externí odkaz:
http://arxiv.org/abs/2408.04300
Image inpainting, which is the task of filling in missing areas in an image, is a common image editing technique. Inpainting can be used to conceal or alter image contents in malicious manipulation of images, driving the need for research in image in
Externí odkaz:
http://arxiv.org/abs/2408.02191
Autor:
Chauhan, Mihir, Satbhai, Abhishek, Hashemi, Mohammad Abuzar, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, Srihari, Sargur
Handwriting Verification is a critical in document forensics. Deep learning based approaches often face skepticism from forensic document examiners due to their lack of explainability and reliance on extensive training data and handcrafted features.
Externí odkaz:
http://arxiv.org/abs/2407.21788
Text-driven image synthesis has made significant advancements with the development of diffusion models, transforming how visual content is generated from text prompts. Despite these advances, text-driven image editing, a key area in computer graphics
Externí odkaz:
http://arxiv.org/abs/2406.00985
Autor:
Chauhan, Mihir, Hashemi, Mohammad Abuzar, Satbhai, Abhishek, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, Srihari, Sargur
We present SSL-HV: Self-Supervised Learning approaches applied to the task of Handwriting Verification. This task involves determining whether a given pair of handwritten images originate from the same or different writer distribution. We have compar
Externí odkaz:
http://arxiv.org/abs/2405.18320
Diffusion-based generative models use stochastic differential equations (SDEs) and their equivalent ordinary differential equations (ODEs) to establish a smooth connection between a complex data distribution and a tractable prior distribution. In thi
Externí odkaz:
http://arxiv.org/abs/2405.11326