Search Results for author: Yuanqing Lin

Found 21 papers, 3 papers with code

Meta-Embeddings Based On Self-Attention

no code implementations3 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.

Language Modelling Machine Translation +3

DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map

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.

Autonomous Driving Camera Pose Estimation +3

Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification

no code implementations12 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.

Action Classification General Classification +2

Deep Metric Learning with Angular Loss

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.

Image Retrieval Metric Learning

Kernel Pooling for Convolutional Neural Networks

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.

Face Recognition Fine-Grained Visual Categorization +2

Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers

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.

Object object-detection +1

Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

no code implementations20 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.

Attribute reinforcement-learning +1

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

1 code implementation16 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.

General Classification Object +4

Fully Convolutional Attention Networks for Fine-Grained Recognition

no code implementations22 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.

reinforcement-learning Reinforcement Learning (RL)

Embedding Label Structures for Fine-Grained Feature Representation

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.

Fine-Grained Image Classification General Classification +3

Fine-grained Image Classification by Exploring Bipartite-Graph Labels

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.

Classification Fine-Grained Image Classification +4

Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification

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.

Fine-Grained Image Classification General Classification +3

Data-Driven 3D Voxel Patterns for Object Category Recognition

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.

Object Object Recognition +1

Object-centric Sampling for Fine-grained Image Classification

no code implementations10 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.

Classification Fine-Grained Image Classification +4

Generic Object Detection With Dense Neural Patterns and Regionlets

no code implementations16 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.

Object object-detection +1

Fine-Grained Visual Categorization via Multi-stage Metric Learning

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.

Fine-Grained Visual Categorization Metric Learning

Analysis of Distributed Stochastic Dual Coordinate Ascent

no code implementations4 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.

Dense Object Reconstruction with Semantic Priors

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.

Object object-detection +2

Deep Coding Network

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.

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