Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Jiang, Xingqun"'
Autor:
Fu, Yuqian, Wang, Yu, Pan, Yixuan, Huai, Lian, Qiu, Xingyu, Shangguan, Zeyu, Liu, Tong, Fu, Yanwei, Van Gool, Luc, Jiang, Xingqun
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promi
Externí odkaz:
http://arxiv.org/abs/2402.03094
Few-shot object detection (FSOD), an efficient method for addressing the severe data-hungry problem, has been extensively discussed. Current works have significantly advanced the problem in terms of model and data. However, the overall performance of
Externí odkaz:
http://arxiv.org/abs/2311.11570
Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to
Externí odkaz:
http://arxiv.org/abs/2211.13495
Deep learning-based object detection has demonstrated a significant presence in the practical applications of artificial intelligence. However, objects such as fire and smoke, pose challenges to object detection because of their non-solid and various
Externí odkaz:
http://arxiv.org/abs/2211.10995
Classic image scaling (e.g. bicubic) can be seen as one convolutional layer and a single upscaling filter. Its implementation is ubiquitous in all display devices and image processing software. In the last decade deep learning systems have been intro
Externí odkaz:
http://arxiv.org/abs/2108.10335
We propose a simple extension of residual networks that works simultaneously in multiple resolutions. Our network design is inspired by the iterative back-projection algorithm but seeks the more difficult task of learning how to enhance images. Compa
Externí odkaz:
http://arxiv.org/abs/2101.10208
Multi-Grid Back-Projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of multi-grid partial differential equation (PDE) solvers this multiscale ar
Externí odkaz:
http://arxiv.org/abs/2101.00150
Convolutional networks are large linear systems divided into layers and connected by non-linear units. These units are the "articulations" that allow the network to adapt to the input. To understand how a network manages to solve a problem we must lo
Externí odkaz:
http://arxiv.org/abs/1908.05168
Autor:
Wang, Tingting, Tang, Xiaojun, Li, Zhexi, Sun, Junmin, Shangguan, Zeyu, Wu, Congrui, Tang, Darun, Liu, Yuyu, Jiang, Xingqun
Publikováno v:
SID Symposium Digest of Technical Papers; Jun2024, Vol. 55 Issue 1, p1199-1202, 4p
Autor:
Ma, Xitong, Wang, Tingting, Li, Xin, Zhang, Shuguo, Duan, Ran, Zhou, Xing, Song, Zheyuan, Jiang, Xingqun
Publikováno v:
SID Symposium Digest of Technical Papers; Jun2024, Vol. 55 Issue 1, p1195-1198, 4p