Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Zhang, Yitian"'
Vision foundation models are renowned for their generalization ability due to massive training data. Nevertheless, they demand tremendous training resources, and the training data is often inaccessible, e.g., CLIP, DINOv2, posing great challenges to
Externí odkaz:
http://arxiv.org/abs/2407.10366
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the pres
Externí odkaz:
http://arxiv.org/abs/2404.13604
Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study
Externí odkaz:
http://arxiv.org/abs/2403.09506
The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens. The patch size controls the ability
Externí odkaz:
http://arxiv.org/abs/2311.04147
Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which
Externí odkaz:
http://arxiv.org/abs/2303.14817
Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is
Externí odkaz:
http://arxiv.org/abs/2211.09992
A deeper network structure generally handles more complicated non-linearity and performs more competitively. Nowadays, advanced network designs often contain a large number of repetitive structures (e.g., Transformer). They empower the network capaci
Externí odkaz:
http://arxiv.org/abs/2210.06699
There have been several recent efforts towards developing representations for multivariate time-series in an unsupervised learning framework. Such representations can prove beneficial in tasks such as activity recognition, health monitoring, and anom
Externí odkaz:
http://arxiv.org/abs/2209.10662
Publikováno v:
In Chemical Engineering Journal 15 October 2024 498
Autor:
Xu, Wenjie, Gao, Xue, Zhang, Menghan, Jiang, Zhengting, Xu, Xiaomin, Huang, Liangfu, Yao, Huiyu, Zhang, Yitian, Tong, Xian, Li, Yuncang, Lin, Jixing, Wen, Cuie, Ding, Xi
Publikováno v:
In Acta Biomaterialia 1 October 2024 187:434-450