Search Results for author: Chunfeng Yuan

Found 16 papers, 3 papers with code

PDNet: Towards Better One-stage Object Detection with Prediction Decoupling

no code implementations28 Apr 2021 Li Yang, Yan Xu, Shaoru Wang, Chunfeng Yuan, Ziqi Zhang, Bing Li, Weiming Hu

However, the most suitable positions for inferring different targets, i. e., the object category and boundaries, are generally different.

Object Detection

Open-book Video Captioning with Retrieve-Copy-Generate Network

no code implementations CVPR 2021 Ziqi Zhang, Zhongang Qi, Chunfeng Yuan, Ying Shan, Bing Li, Ying Deng, Weiming Hu

Due to the rapid emergence of short videos and the requirement for content understanding and creation, the video captioning task has received increasing attention in recent years.

Video Captioning

DIFER: Differentiable Automated Feature Engineering

no code implementations17 Oct 2020 Guanghui Zhu, Zhuoer Xu, Xu Guo, Chunfeng Yuan, Yihua Huang

Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.

Automated Feature Engineering Feature Engineering

Object Relational Graph with Teacher-Recommended Learning for Video Captioning

no code implementations CVPR 2020 Ziqi Zhang, Yaya Shi, Chunfeng Yuan, Bing Li, Peijin Wang, Weiming Hu, Zheng-Jun Zha

In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy.

 Ranked #1 on Video Captioning on MSR-VTT (using extra training data)

Language Modelling Video Captioning

Multimodal Semantic Attention Network for Video Captioning

no code implementations8 May 2019 Liang Sun, Bing Li, Chunfeng Yuan, Zheng-Jun Zha, Weiming Hu

Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.

General Classification Multi-Label Classification +1

Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification

no code implementations ECCV 2018 Yang Du, Chunfeng Yuan, Bing Li, Lili Zhao, Yangxi Li, Weiming Hu

Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps.

Action Classification Classification +1

Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection From Videos

no code implementations CVPR 2017 Yang Du, Chunfeng Yuan, Bing Li, Weiming Hu, Stephen Maybank

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background.

Object Detection

Multi-target Tracking with Motion Context in Tensor Power Iteration

no code implementations CVPR 2014 Xinchu Shi, Haibin Ling, Weiming Hu, Chunfeng Yuan, Junliang Xing

In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization.

Human Action Recognition Based on Context-Dependent Graph Kernels

no code implementations CVPR 2014 Baoxin Wu, Chunfeng Yuan, Weiming Hu

Then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model.

Action Recognition

Multi-task Sparse Learning with Beta Process Prior for Action Recognition

no code implementations CVPR 2013 Chunfeng Yuan, Weiming Hu, Guodong Tian, Shuang Yang, Haoran Wang

In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible.

Action Recognition Sparse Learning

3D R Transform on Spatio-temporal Interest Points for Action Recognition

no code implementations CVPR 2013 Chunfeng Yuan, Xi Li, Weiming Hu, Haibin Ling, Stephen Maybank

In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points.

Action Recognition

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