1 code implementation • 13 May 2024 • Liuxin Bao, Xiaofei Zhou, Xiankai Lu, Yaoqi Sun, Haibing Yin, Zhenghui Hu, Jiyong Zhang, Chenggang Yan
Therefore, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet.
1 code implementation • 24 Aug 2022 • Qi Wei, Haoliang Sun, Xiankai Lu, Yilong Yin
Sample selection is an effective strategy to mitigate the effect of label noise in robust learning.
no code implementations • CVPR 2022 • Rundong He, Zhongyi Han, Xiankai Lu, Yilong Yin
To take advantage of these unseen-class data and ensure performance, we propose a safe SSL method called SAFE-STUDENT from the teacher-student view.
no code implementations • CVPR 2022 • Zheyun Qin, Xiankai Lu, Xiushan Nie, Yilong Yin, Jianbing Shen
Video Instance Segmentation (VIS) needs to automatically track and segment multiple objects in videos that rely on modeling the spatial-temporal interactions of the instances.
no code implementations • CVPR 2021 • Wenbin Ge, Xiankai Lu, Jianbing Shen
In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective.
1 code implementation • 14 May 2021 • Haoliang Sun, Xiankai Lu, Haochen Wang, Yilong Yin, XianTong Zhen, Cees G. M. Snoek, Ling Shao
We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution.
1 code implementation • ECCV 2020 • Xiankai Lu, Wenguan Wang, Martin Danelljan, Tianfei Zhou, Jianbing Shen, Luc van Gool
How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segmentation.
1 code implementation • 1 Jun 2020 • Tao Zhou, Huazhu Fu, Yu Zhang, Changqing Zhang, Xiankai Lu, Jianbing Shen, Ling Shao
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
1 code implementation • CVPR 2020 • Xiankai Lu, Wenguan Wang, Jianbing Shen, Yu-Wing Tai, David Crandall, Steven C. H. Hoi
We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data.
1 code implementation • ICCV 2019 • Wenguan Wang, Xiankai Lu, Jianbing Shen, David Crandall, Ling Shao
Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higher-order relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation.
1 code implementation • CVPR 2019 • Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, Fatih Porikli
We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view.
Semantic Segmentation Unsupervised Video Object Segmentation +2
1 code implementation • ICCV 2019 • Ziyi Shen, Wenguan Wang, Xiankai Lu, Jianbing Shen, Haibin Ling, Tingfa Xu, Ling Shao
This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG).
no code implementations • 24 Jul 2019 • Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi
This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools.
1 code implementation • ECCV 2018 • Xiankai Lu, Chao Ma, Bingbing Ni, Xiaokang Yang, Ian Reid, Ming-Hsuan Yang
Regression trackers directly learn a mapping from regularly dense samples of target objects to soft labels, which are usually generated by a Gaussian function, to estimate target positions.
no code implementations • 15 Oct 2014 • Xiankai Lu, Zheng Fang, Tao Xu, Haiting Zhang, Hongya Tuo
In object recognition, Fisher vector (FV) representation is one of the state-of-art image representations ways at the expense of dense, high dimensional features and increased computation time.
no code implementations • 15 Oct 2014 • Wenya Zhu, Xiankai Lu, Tao Xu, Ziyi Zhao
It is well known that scene images are well characterized by particular arrangements of their local structures, we divide the scene image into the non-overlapping sub-regions and compute the proposed higher order structural features among them.