Search Results for author: Wei-Ming Dong

Found 19 papers, 5 papers with code

A design of human-like robust AI machines in object identification

no code implementations7 Jan 2021 Bao-Gang Hu, Wei-Ming Dong

Similar to the perspective, or design, position by Turing, we provide a solution of how to achieve HLR AI machines without constructing them and conducting real experiments.

Causal Inference Common Sense Reasoning +1

Arbitrary Video Style Transfer via Multi-Channel Correlation

no code implementations17 Sep 2020 Yingying Deng, Fan Tang, Wei-Ming Dong, Haibin Huang, Chongyang Ma, Changsheng Xu

Towards this end, we propose Multi-Channel Correction network (MCCNet), which can be trained to fuse the exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos.

Style Transfer Video Style Transfer

Distribution Aligned Multimodal and Multi-Domain Image Stylization

no code implementations2 Jun 2020 Minxuan Lin, Fan Tang, Wei-Ming Dong, Xiao Li, Chongyang Ma, Changsheng Xu

Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously.

Image Stylization

Arbitrary Style Transfer via Multi-Adaptation Network

2 code implementations27 May 2020 Yingying Deng, Fan Tang, Wei-Ming Dong, Wen Sun, Feiyue Huang, Changsheng Xu

Arbitrary style transfer is a significant topic with research value and application prospect.

Disentanglement Style Transfer

Dynamic Refinement Network for Oriented and Densely Packed Object Detection

1 code implementation CVPR 2020 Xingjia Pan, Yuqiang Ren, Kekai Sheng, Wei-Ming Dong, Haolei Yuan, Xiaowei Guo, Chongyang Ma, Changsheng Xu

However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.

feature selection object-detection +2

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

no code implementations26 Nov 2019 Kekai Sheng, Wei-Ming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma

In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective.

Incremental Concept Learning via Online Generative Memory Recall

no code implementations5 Jul 2019 Huaiyu Li, Wei-Ming Dong, Bao-Gang Hu

The main reason for catastrophic forgetting is that the past concept data is not available and neural weights are changed during incrementally learning new concepts.

Class Incremental Learning Generative Adversarial Network +1

LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

1 code implementation15 May 2019 Huaiyu Li, Wei-Ming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu

The TargetNet module is a neural network for solving a specific task and the MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples.

Few-Shot Learning

"Ge Shu Zhi Zhi": Towards Deep Understanding about Worlds

no code implementations19 Dec 2018 Bao-Gang Hu, Wei-Ming Dong

"Ge She Zhi Zhi" is a novel saying in Chinese, stated as "To investigate things from the underlying principle(s) and to acquire knowledge in the form of mathematical representations".


Gourmet Photography Dataset for Aesthetic Assessment of Food Images

1 code implementation SIGGRAPH Asia 2018 2018 Kekai Sheng, Wei-Ming Dong, Haibin Huang, Chongyang Ma, Bao-Gang Hu

In this study, we present the Gourmet Photography Dataset (GPD), which is the first large-scale dataset for aesthetic assessment of food photographs.

Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment

1 code implementation ACM Multimedia Conference 2018 Kekai Sheng, Wei-Ming Dong, Chongyang Ma, Xing Mei, Feiyue Huang, Bao-Gang Hu

Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data.

Aesthetics Quality Assessment

Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation

no code implementations CVPR 2018 Yong Zhang, Rui Zhao, Wei-Ming Dong, Bao-Gang Hu, Qiang Ji

The majority of methods directly apply supervised learning techniques to AU intensity estimation while few methods exploit unlabeled samples to improve the performance.


Classifier Learning With Prior Probabilities for Facial Action Unit Recognition

no code implementations CVPR 2018 Yong Zhang, Wei-Ming Dong, Bao-Gang Hu, Qiang Ji

To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities.

Anatomy Facial Action Unit Detection

Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation

no code implementations CVPR 2018 Yong Zhang, Wei-Ming Dong, Bao-Gang Hu, Qiang Ji

Facial action unit (AU) intensity estimation plays an important role in affective computing and human-computer interaction.

Image Retargetability

no code implementations12 Feb 2018 Fan Tang, Wei-Ming Dong, Yiping Meng, Chongyang Ma, Fuzhang Wu, Xinrui Li, Tong-Yee Lee

In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting.

Image Retargeting

A study on cost behaviors of binary classification measures in class-imbalanced problems

no code implementations26 Mar 2014 Bao-Gang Hu, Wei-Ming Dong

Based on their cost functions, we are able to conclude that G-means of accuracy rates and BER are suitable measures because they show "proper" cost behaviors in terms of "a misclassification from a small class will cause a greater cost than that from a large class".

Binary Classification General Classification

Segment-Tree Based Cost Aggregation for Stereo Matching

no code implementations CVPR 2013 Xing Mei, Xun Sun, Wei-Ming Dong, Haitao Wang, Xiaopeng Zhang

Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation.

Scene Segmentation Stereo Matching +1

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