Zobrazeno 1 - 10
of 553
pro vyhledávání: '"He, Wenbin"'
The emergence of large-scale pre-trained models has heightened their application in various downstream tasks, yet deployment is a challenge in environments with limited computational resources. Knowledge distillation has emerged as a solution in such
Externí odkaz:
http://arxiv.org/abs/2406.17838
Autor:
Wang, Xiaocong, Wang, Benhai, He, Wenbin, Zhang, Xintong, Huang, Qi, Huang, Zhiyuan, Jiang, Xin, Russell, Philip St. J., Pang, Meng
Harmonic mode-locking, realized actively or passively, is an effective technique for increasing the repetition rate of lasers, with important applications in optical sampling, laser micro-machining and frequency metrology. It is critically important
Externí odkaz:
http://arxiv.org/abs/2406.10026
Autor:
Wang, Xiaoqi, He, Wenbin, Xuan, Xiwei, Sebastian, Clint, Ono, Jorge Piazentin, Li, Xin, Behpour, Sima, Doan, Thang, Gou, Liang, Shen, Han Wei, Ren, Liu
The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (S
Externí odkaz:
http://arxiv.org/abs/2406.05271
Autor:
Chen, Tiandao, Pan, Jinyu, Huang, Zhiyuan, Yu, Yue, Liu, Donghan, Chang, Xinshuo, Liu, Zhengzheng, He, Wenbin, Jiang, Xin, Pang, Meng, Leng, Yuxin, Li, Ruxin
Coherent dispersive wave emission, as an important phenomenon of soliton dynamics, manifests itself in multiple platforms of nonlinear optics from fibre waveguides to integrated photonics. Limited by its resonance nature, efficient generation of cohe
Externí odkaz:
http://arxiv.org/abs/2403.12347
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting. The rise of Vision-Language models (VLMs) has unlocked numerous applications, leveragi
Externí odkaz:
http://arxiv.org/abs/2403.06295
Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement. Visual concept-based methods, while increasingly used for this purpose, face challenges: (1) most concepts la
Externí odkaz:
http://arxiv.org/abs/2311.03547
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications. However, existing OOD detection approaches primarily rely on the feature maps or the full gradient space infor
Externí odkaz:
http://arxiv.org/abs/2308.00310
In this study, we investigate the task of data pre-selection, which aims to select instances for labeling from an unlabeled dataset through a single pass, thereby optimizing performance for undefined downstream tasks with a limited annotation budget.
Externí odkaz:
http://arxiv.org/abs/2307.11227
Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown objects while integrating learned knowledge for future tasks. However
Externí odkaz:
http://arxiv.org/abs/2306.14291
To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Speci
Externí odkaz:
http://arxiv.org/abs/2306.05281