Zobrazeno 1 - 10
of 5 516
pro vyhledávání: '"ZHENG, Jin"'
Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. To address this issue, we pr
Externí odkaz:
http://arxiv.org/abs/2412.04034
One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to trai
Externí odkaz:
http://arxiv.org/abs/2410.20807
The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn f
Externí odkaz:
http://arxiv.org/abs/2410.19239
Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning
Externí odkaz:
http://arxiv.org/abs/2407.06045
Autor:
Zheng, Jin-Hao, Wang, Qin-Qin, Feng, Lan-Tian, Ding, Yu-Yang, Xu, Xiao-Ye, Ren, Xi-Feng, Li, Chuan-Feng, Guo, Guang-Can
Publikováno v:
Phys. Rev. Applied 22, 054011 (2024)
The growing maturity of photonic integrated circuit (PIC) fabrication technology enables the high integration of an increasing number of optical components onto a single chip. With the incremental circuit complexity, the calibration of active phase s
Externí odkaz:
http://arxiv.org/abs/2407.02207
3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively impacting rendering. This paper introduces a new co-regularization per
Externí odkaz:
http://arxiv.org/abs/2405.12110
Autor:
Liu, Jian, Sun, Wei, Yang, Hui, Zeng, Zhiwen, Liu, Chongpei, Zheng, Jin, Liu, Xingyu, Rahmani, Hossein, Sebe, Nicu, Mian, Ajmal
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted convent
Externí odkaz:
http://arxiv.org/abs/2405.07801
Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D talking heads. However, due to the difficulty in fitting steep appearance changes, the prevailing paradigm that presents facial motions by directly modifying point a
Externí odkaz:
http://arxiv.org/abs/2404.15264
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories,
Externí odkaz:
http://arxiv.org/abs/2404.03248
With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However, few studies have investigated the robustness after fine-tuning them in real-world scenari
Externí odkaz:
http://arxiv.org/abs/2403.07705