Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Cai, Yuxuan"'
Autor:
Cai, Yuxuan, Zhang, Jiangning, He, Haoyang, He, Xinwei, Tong, Ao, Gan, Zhenye, Wang, Chengjie, Bai, Xiang
The success of Large Language Models (LLM) has led researchers to explore Multimodal Large Language Models (MLLM) for unified visual and linguistic understanding. However, the increasing model size and computational complexity of MLLM limit their use
Externí odkaz:
http://arxiv.org/abs/2410.16236
Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models. However, despite these efforts, the available information an
Externí odkaz:
http://arxiv.org/abs/2410.15458
Autor:
Huang, Tingfeng, Cheng, Yuxuan, Xia, Jingbo, Yu, Rui, Cai, Yuxuan, Xiang, Jinhai, He, Xinwei, Bai, Xiang
Reconstruction-based methods have significantly advanced modern unsupervised anomaly detection. However, the strong capacity of neural networks often violates the underlying assumptions by reconstructing abnormal samples well. To alleviate this issue
Externí odkaz:
http://arxiv.org/abs/2408.07490
Autor:
Zhang, Jiangning, He, Haoyang, Gan, Zhenye, He, Qingdong, Cai, Yuxuan, Xue, Zhucun, Wang, Yabiao, Wang, Chengjie, Xie, Lei, Liu, Yong
Visual anomaly detection aims to identify anomalous regions in images through unsupervised learning paradigms, with increasing application demand and value in fields such as industrial inspection and medical lesion detection. Despite significant prog
Externí odkaz:
http://arxiv.org/abs/2406.03262
Autor:
Hu, JiaKui, Yao, Man, Qiu, Xuerui, Chou, Yuhong, Cai, Yuxuan, Qiao, Ning, Tian, Yonghong, XU, Bo, Li, Guoqi
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This w
Externí odkaz:
http://arxiv.org/abs/2405.16466
Recently, large vision and language models have shown their success when adapting them to many downstream tasks. In this paper, we present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. To this end, we
Externí odkaz:
http://arxiv.org/abs/2403.09493
Autor:
AI, 01., Young, Alex, Chen, Bei, Li, Chao, Huang, Chengen, Zhang, Ge, Zhang, Guanwei, Li, Heng, Zhu, Jiangcheng, Chen, Jianqun, Chang, Jing, Yu, Kaidong, Liu, Peng, Liu, Qiang, Yue, Shawn, Yang, Senbin, Yang, Shiming, Yu, Tao, Xie, Wen, Huang, Wenhao, Hu, Xiaohui, Ren, Xiaoyi, Niu, Xinyao, Nie, Pengcheng, Xu, Yuchi, Liu, Yudong, Wang, Yue, Cai, Yuxuan, Gu, Zhenyu, Liu, Zhiyuan, Dai, Zonghong
We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long
Externí odkaz:
http://arxiv.org/abs/2403.04652
Defect detection is a critical research area in artificial intelligence. Recently, synthetic data-based self-supervised learning has shown great potential on this task. Although many sophisticated synthesizing strategies exist, little research has be
Externí odkaz:
http://arxiv.org/abs/2310.07585
Masked image modeling (MIM) has become a prevalent pre-training setup for vision foundation models and attains promising performance. Despite its success, existing MIM methods discard the decoder network during downstream applications, resulting in i
Externí odkaz:
http://arxiv.org/abs/2309.01005
We propose a new neural network design paradigm Reversible Column Network (RevCol). The main body of RevCol is composed of multiple copies of subnetworks, named columns respectively, between which multi-level reversible connections are employed. Such
Externí odkaz:
http://arxiv.org/abs/2212.11696