Search Results for author: Yong Dou

Found 14 papers, 1 papers with code

Argumentation Mining on Essays at Multi Scales

no code implementations COLING 2020 Hao Wang, Zhen Huang, Yong Dou, Yu Hong

Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level.

Fixed-size Objects Encoding for Visual Relationship Detection

no code implementations29 May 2020 Hengyue Pan, Xin Niu, Rongchun Li, Siqi Shen, Yong Dou

Instead, we propose a novel method to encode all background objects in each image by using one fixed-size vector (i. e., FBE vector).

General Classification Predicate Classification +1

Towards Precise End-to-end Weakly Supervised Object Detection Network

1 code implementation ICCV 2019 Ke Yang, Dongsheng Li, Yong Dou

It is challenging for weakly supervised object detection network to precisely predict the positions of the objects, since there are no instance-level category annotations.

Multiple Instance Learning Weakly Supervised Object Detection

Heavy-ball Algorithms Always Escape Saddle Points

no code implementations23 Jul 2019 Tao Sun, Dongsheng Li, Zhe Quan, Hao Jiang, Shengguo Li, Yong Dou

In this paper, we answer a question: can the nonconvex heavy-ball algorithms with random initialization avoid saddle points?

Visual Confusion Label Tree For Image Classification

no code implementations5 Jun 2019 Yuntao Liu, Yong Dou, Ruochun Jin, Rongchun Li

In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier.

Classification General Classification +1

Visual Tree Convolutional Neural Network in Image Classification

no code implementations4 Jun 2019 Yuntao Liu, Yong Dou, Ruochun Jin, Peng Qiao

In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning.

Classification General Classification +1

IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition

no code implementations26 Feb 2019 Ke Yang, Peng Qiao, Dongsheng Li, Yong Dou

Focusing on discriminate spatiotemporal feature learning, we propose Information Fused Temporal Transformation Network (IF-TTN) for action recognition on top of popular Temporal Segment Network (TSN) framework.

Action Recognition Optical Flow Estimation

Exploring Frame Segmentation Networks for Temporal Action Localization

no code implementations14 Feb 2019 Ke Yang, Xiaolong Shen, Peng Qiao, Shijie Li, Dongsheng Li, Yong Dou

The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.

Temporal Action Localization

DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

no code implementations16 Nov 2018 Hengyue Pan, Hui Jiang, Xin Niu, Yong Dou

Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks.

Image Classification

Learning Generic Diffusion Processes for Image Restoration

no code implementations17 Jul 2018 Peng Qiao, Yong Dou, Yunjin Chen, Wensen Feng

On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.

Denoising Image Restoration

Exploring Temporal Preservation Networks for Precise Temporal Action Localization

no code implementations10 Aug 2017 Ke Yang, Peng Qiao, Dongsheng Li, Shaohe Lv, Yong Dou

A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization.

Temporal Action Localization Temporal Localization

Learning Non-local Image Diffusion for Image Denoising

no code implementations24 Feb 2017 Peng Qiao, Yong Dou, Wensen Feng, Yunjin Chen

In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.

Image Denoising SSIM

Weakly supervised object detection using pseudo-strong labels

no code implementations16 Jul 2016 Ke Yang, Dongsheng Li, Yong Dou, Shaohe Lv, Qiang Wang

Object detection is an import task of computer vision. A variety of methods have been proposed, but methods using the weak labels still do not have a satisfactory result. In this paper, we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised model. One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset. A de-noise method is then applied to the noisy bounding boxes. Then the de-noised pseudo-strong labels are used to train a strongly object detection network. The whole framework is still weakly supervised because the entire process only uses the image-level labels. The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43. 4% on mean average precision compared to 39. 5% of the previous best result and 34. 5% of the initial method, respectively. And this frame work is simple and distinct, and is promising to be applied to other method easily.

Weakly Supervised Object Detection

Relative distance features for gait recognition with Kinect

no code implementations18 May 2016 Ke Yang, Yong Dou, Shaohe Lv, Fei Zhang, Qi Lv

This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation -- relative distance-based gait features.

Gait Recognition

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