no code implementations • ICLR 2022 • Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, Yuanqing Lin
The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm.
no code implementations • 3 Mar 2020 • Qichen Li, Yuanqing Lin, Luofeng Zhou, Jian Li
Creating meta-embeddings for better performance in language modelling has received attention lately, and methods based on concatenation or merely calculating the arithmetic mean of more than one separately trained embeddings to perform meta-embeddings have shown to be beneficial.
1 code implementation • CVPR 2018 • Peng Wang, Ruigang Yang, Binbin Cao, Wei Xu, Yuanqing Lin
The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system.
no code implementations • 12 Aug 2017 • Yunlong Bian, Chuang Gan, Xiao Liu, Fu Li, Xiang Long, Yandong Li, Heng Qi, Jie zhou, Shilei Wen, Yuanqing Lin
Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks.
Ranked #162 on Action Classification on Kinetics-400
1 code implementation • ICCV 2017 • Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, Yuanqing Lin
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images.
no code implementations • CVPR 2017 • Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, Serge Belongie
We demonstrate how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner.
no code implementations • CVPR 2016 • Fan Yang, Wongun Choi, Yuanqing Lin
In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layer-wise cascaded rejection classifiers.
no code implementations • 20 May 2016 • Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, Yuanqing Lin
By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm.
1 code implementation • 16 Apr 2016 • Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese
In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation.
Ranked #4 on Vehicle Pose Estimation on KITTI Cars Hard
no code implementations • 22 Mar 2016 • Xiao Liu, Tian Xia, Jiang Wang, Yi Yang, Feng Zhou, Yuanqing Lin
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses.
no code implementations • CVPR 2016 • Yin Cui, Feng Zhou, Yuanqing Lin, Serge Belongie
To demonstrate the effectiveness of the proposed framework, we bootstrap a fine-grained flower dataset with 620 categories from Instagram images.
no code implementations • CVPR 2016 • Xiaofan Zhang, Feng Zhou, Yuanqing Lin, Shaoting Zhang
However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e. g., discovering cars from the same make or the same model, both of which require high precision.
no code implementations • CVPR 2016 • Feng Zhou, Yuanqing Lin
To facilitate the study, we construct a new food benchmark dataset, which consists of 37, 885 food images collected from 6 restaurants and totally 975 menus.
no code implementations • CVPR 2015 • Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese
Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image.
no code implementations • CVPR 2015 • Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin
We demonstrate the success of the proposed framework on two small-scale fine-grained datasets (Stanford Dogs and Stanford Cars) and on a large-scale car dataset that we collected.
no code implementations • 10 Dec 2014 • Xiaoyu Wang, Tianbao Yang, Guobin Chen, Yuanqing Lin
In contrast, this paper proposes an \emph{object-centric sampling} (OCS) scheme that samples image windows based on the object location information.
no code implementations • 16 Apr 2014 • Will Y. Zou, Xiaoyu Wang, Miao Sun, Yuanqing Lin
This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection.
no code implementations • CVPR 2015 • Qi Qian, Rong Jin, Shenghuo Zhu, Yuanqing Lin
To this end, we proposed a multi-stage metric learning framework that divides the large-scale high dimensional learning problem to a series of simple subproblems, achieving $\mathcal{O}(d)$ computational complexity.
no code implementations • 4 Dec 2013 • Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin
Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates.
no code implementations • CVPR 2013 • Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin, Silvio Savarese
Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape.
no code implementations • NeurIPS 2010 • Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu
This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is derived based on theoretical analysis of nonlinear functional approximation that extends recent results for local coordinate coding.