Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Yu, Peiyu"'
Autor:
Jiang, Eric Hanchen, Zhang, Zhi, Zhang, Dinghuai, Lizarraga, Andrew, Xu, Chenheng, Zhang, Yasi, Zhao, Siyan, Xu, Zhengjie, Yu, Peiyu, Tang, Yuer, Kong, Deqian, Wu, Ying Nian
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world models with decision transformers remains a challenge. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.11359
Autor:
Zhao, Haozhe, Ma, Xiaojian, Chen, Liang, Si, Shuzheng, Wu, Rujie, An, Kaikai, Yu, Peiyu, Zhang, Minjia, Li, Qing, Chang, Baobao
This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2
Externí odkaz:
http://arxiv.org/abs/2407.05282
Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the instantaneous change-of-variables formula, one can directly compute imag
Externí odkaz:
http://arxiv.org/abs/2405.18816
Autor:
Yu, Peiyu, Zhang, Dinghuai, He, Hengzhi, Ma, Xiaojian, Miao, Ruiyao, Lu, Yifan, Zhang, Yasi, Kong, Deqian, Gao, Ruiqi, Xie, Jianwen, Cheng, Guang, Wu, Ying Nian
Offline Black-Box Optimization (BBO) aims at optimizing a black-box function using the knowledge from a pre-collected offline dataset of function values and corresponding input designs. However, the high-dimensional and highly-multimodal input design
Externí odkaz:
http://arxiv.org/abs/2405.16730
In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity, and also
Externí odkaz:
http://arxiv.org/abs/2405.14018
Text-to-image diffusion models have shown great success in generating high-quality text-guided images. Yet, these models may still fail to semantically align generated images with the provided text prompts, leading to problems like incorrect attribut
Externí odkaz:
http://arxiv.org/abs/2404.07389
Visual planning simulates how humans make decisions to achieve desired goals in the form of searching for visual causal transitions between an initial visual state and a final visual goal state. It has become increasingly important in egocentric visi
Externí odkaz:
http://arxiv.org/abs/2310.03325
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in the field of generative modeling due to its flexibility in the formulation and strong modeling power of the latent space. However, the common
Externí odkaz:
http://arxiv.org/abs/2310.03218
Autor:
Yu, Peiyu, Xie, Sirui, Ma, Xiaojian, Jia, Baoxiong, Pang, Bo, Gao, Ruiqi, Zhu, Yixin, Zhu, Song-Chun, Wu, Ying Nian
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works built upon it ha
Externí odkaz:
http://arxiv.org/abs/2206.05895
We present Deep Region Competition (DRC), an algorithm designed to extract foreground objects from images in a fully unsupervised manner. Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying
Externí odkaz:
http://arxiv.org/abs/2110.15497