Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Huang, Lianghua"'
Autor:
Huang, Lianghua, Wang, Wei, Wu, Zhi-Fan, Dou, Huanzhang, Shi, Yupeng, Feng, Yutong, Liang, Chen, Liu, Yu, Zhou, Jingren
While large language models (LLMs) have revolutionized natural language processing with their task-agnostic capabilities, visual generation tasks such as image translation, style transfer, and character customization still rely heavily on supervised,
Externí odkaz:
http://arxiv.org/abs/2410.15027
Autor:
Zhang, Shilong, Huang, Lianghua, Chen, Xi, Zhang, Yifei, Wu, Zhi-Fan, Feng, Yutong, Wang, Wei, Shen, Yujun, Liu, Yu, Luo, Ping
This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customizat
Externí odkaz:
http://arxiv.org/abs/2403.17008
Vector-quantized image modeling has shown great potential in synthesizing high-quality images. However, generating high-resolution images remains a challenging task due to the quadratic computational overhead of the self-attention process. In this st
Externí odkaz:
http://arxiv.org/abs/2310.05400
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations in a harmonious way. Instead of tuning parameters for each object, our model is trained only once and ef
Externí odkaz:
http://arxiv.org/abs/2307.09481
Autor:
Yang, Zhantao, Feng, Ruili, Zhang, Han, Shen, Yujun, Zhu, Kai, Huang, Lianghua, Zhang, Yifei, Liu, Yu, Zhao, Deli, Zhou, Jingren, Cheng, Fan
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models. However, the rationality of the diffusion process itself receives limited attention, leaving t
Externí odkaz:
http://arxiv.org/abs/2306.11251
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability. This work offers a new generation paradigm that allows flexible control of the output image, such as spati
Externí odkaz:
http://arxiv.org/abs/2302.09778
Autor:
Zhang, Han, Feng, Ruili, Yang, Zhantao, Huang, Lianghua, Liu, Yu, Zhang, Yifei, Shen, Yujun, Zhao, Deli, Zhou, Jingren, Cheng, Fan
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, considering the spatial redundancy in image signals, there is no need to
Externí odkaz:
http://arxiv.org/abs/2211.16032
Recent generative models show impressive results in photo-realistic image generation. However, artifacts often inevitably appear in the generated results, leading to downgraded user experience and reduced performance in downstream tasks. This work ai
Externí odkaz:
http://arxiv.org/abs/2210.08573
Target tracking, the essential ability of the human visual system, has been simulated by computer vision tasks. However, existing trackers perform well in austere experimental environments but fail in challenges like occlusion and fast motion. The ma
Externí odkaz:
http://arxiv.org/abs/2202.13073
Autor:
Huang, Lianghua, Pan, Zhaoji, Pan, Chuanyan, Zhao, Longyan, Zhong, Shengping, Gao, Chenghai, Mi, Shunli, Feng, Pengfei, Deng, Guoqing, Meng, Yaowen, Yang, Xueming, Chen, Xiuli, Yu, Yongxiang
Publikováno v:
In Aquaculture Reports June 2024 36
