Zobrazeno 1 - 10
of 277
pro vyhledávání: '"Chen, BingHui"'
Recently, several point-based image editing methods (e.g., DragDiffusion, FreeDrag, DragNoise) have emerged, yielding precise and high-quality results based on user instructions. However, these methods often make insufficient use of semantic informat
Externí odkaz:
http://arxiv.org/abs/2410.12696
Due to the significant advances in large-scale text-to-image generation by diffusion model (DM), controllable human image generation has been attracting much attention recently. Existing works, such as Controlnet [36], T2I-adapter [20] and HumanSD [1
Externí odkaz:
http://arxiv.org/abs/2405.09985
With the development of the large-scale diffusion model, Artificial Intelligence Generated Content (AIGC) techniques are popular recently. However, how to truly make it serve our daily lives remains an open question. To this end, in this paper, we fo
Externí odkaz:
http://arxiv.org/abs/2404.04833
Customized generative text-to-image models have the ability to produce images that closely resemble a given subject. However, in the context of generating advertising images for e-commerce scenarios, it is crucial that the generated subject's identit
Externí odkaz:
http://arxiv.org/abs/2404.04828
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems,2024
Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all domains, which is known as domain bias. In this study, we propose a si
Externí odkaz:
http://arxiv.org/abs/2402.03758
Crowd counting has achieved significant progress by training regressors to predict instance positions. In heavily crowded scenarios, however, regressors are challenged by uncontrollable annotation variance, which causes density map bias and context i
Externí odkaz:
http://arxiv.org/abs/2312.01711
Autor:
He, Jun-Yan, Cheng, Zhi-Qi, Li, Chenyang, Xiang, Wangmeng, Chen, Binghui, Luo, Bin, Geng, Yifeng, Xie, Xuansong
Publikováno v:
In the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)
Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research. To address this gap, we present DAMO-StreamNet, an optimized framework that combines recent advances
Externí odkaz:
http://arxiv.org/abs/2303.17144
Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of
Externí odkaz:
http://arxiv.org/abs/2212.03943
Publikováno v:
In Chemical Engineering Science 5 February 2025 302 Part B
Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data. Though various self-training based and consistency-regularization based SSOD methods have be
Externí odkaz:
http://arxiv.org/abs/2204.07300