Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Gan, Yulu"'
Autor:
Dai, Gaole, Tang, Yiming, Fan, Chunkai, Zhang, Qizhe, Zhang, Zhi, Gan, Yulu, Zeng, Chengqing, Zhang, Shanghang, Huang, Tiejun
Pre-trained Artificial Neural Networks (ANNs) exhibit robust pattern recognition capabilities and share extensive similarities with the human brain, specifically Biological Neural Networks (BNNs). We are particularly intrigued by these models' abilit
Externí odkaz:
http://arxiv.org/abs/2409.06706
Autor:
Chen, Anthony, Yang, Huanrui, Gan, Yulu, Gudovskiy, Denis A, Dong, Zhen, Wang, Haofan, Okuno, Tomoyuki, Nakata, Yohei, Keutzer, Kurt, Zhang, Shanghang
Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce uncalibrated closed-set predictions for OOD data. A more robust class
Externí odkaz:
http://arxiv.org/abs/2312.09148
Autor:
Shen, Cuifeng, Gan, Yulu, Chen, Chen, Zhu, Xiongwei, Cheng, Lele, Gao, Tingting, Wang, Jinzhi
The goal of conditional image-to-video (cI2V) generation is to create a believable new video by beginning with the condition, i.e., one image and text.The previous cI2V generation methods conventionally perform in RGB pixel space, with limitations in
Externí odkaz:
http://arxiv.org/abs/2311.14294
Autor:
Huang, Peixiang, Zhang, Songtao, Gan, Yulu, Xu, Rui, Zhu, Rongqi, Qin, Wenkang, Guo, Limei, Jiang, Shan, Luo, Lin
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various im
Externí odkaz:
http://arxiv.org/abs/2310.20427
Recent advances in generative diffusion models have enabled text-controlled synthesis of realistic and diverse images with impressive quality. Despite these remarkable advances, the application of text-to-image generative models in computer vision fo
Externí odkaz:
http://arxiv.org/abs/2310.00390
Autor:
Pan, Mingjie, Gan, Yulu, Zhou, Fangxu, Liu, Jiaming, Wang, Aimin, Zhang, Shanghang, Li, Dawei
Three-dimensional microscopy is often limited by anisotropic spatial resolution, resulting in lower axial resolution than lateral resolution. Current State-of-The-Art (SoTA) isotropic reconstruction methods utilizing deep neural networks can achieve
Externí odkaz:
http://arxiv.org/abs/2306.12109
Language-Image Pre-training has demonstrated promising results on zero-shot and few-shot downstream tasks by prompting visual models with natural language prompts. However, most recent studies only use a single prompt for tuning, neglecting the inher
Externí odkaz:
http://arxiv.org/abs/2304.07919
Autor:
Yang, Senqiao, Wu, Jiarui, Liu, Jiaming, Li, Xiaoqi, Zhang, Qizhe, Pan, Mingjie, Gan, Yulu, Chen, Zehui, Zhang, Shanghang
The visual prompts have provided an efficient manner in addressing visual cross-domain problems. In previous works, Visual Domain Prompt (VDP) first introduces domain prompts to tackle the classification Test-Time Adaptation (TTA) problem by warping
Externí odkaz:
http://arxiv.org/abs/2303.09792
Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predic
Externí odkaz:
http://arxiv.org/abs/2212.04145
Autor:
Gan, Yulu, Pan, Mingjie, Zhang, Rongyu, Ling, Zijian, Zhao, Lingran, Liu, Jiaming, Zhang, Shanghang
When facing changing environments in the real world, the lightweight model on client devices suffers from severe performance drops under distribution shifts. The main limitations of the existing device model lie in (1) unable to update due to the com
Externí odkaz:
http://arxiv.org/abs/2212.00972