Search Results for author: Chen Fang

Found 39 papers, 13 papers with code

Video Object Detection via Object-level Temporal Aggregation

no code implementations ECCV 2020 Chun-Han Yao, Chen Fang, Xiaohui Shen, Yangyue Wan, Ming-Hsuan Yang

While single-image object detectors can be naively applied to videos in a frame-by-frame fashion, the prediction is often temporally inconsistent.

Video Object Detection

Instances as Queries

5 code implementations ICCV 2021 Yuxin Fang, Shusheng Yang, Xinggang Wang, Yu Li, Chen Fang, Ying Shan, Bin Feng, Wenyu Liu

The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage.

Instance Segmentation Object Detection +1

Crossover Learning for Fast Online Video Instance Segmentation

1 code implementation ICCV 2021 Shusheng Yang, Yuxin Fang, Xinggang Wang, Yu Li, Chen Fang, Ying Shan, Bin Feng, Wenyu Liu

For temporal information modeling in VIS, we present a novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames.

Instance Segmentation Semantic Segmentation +2

Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks

no code implementations6 May 2020 Wei Xiong, Ding Liu, Xiaohui Shen, Chen Fang, Jiebo Luo

Conventional learning-based approaches to low-light image enhancement typically require a large amount of paired training data, which are difficult to acquire in real-world scenarios.

Low-Light Image Enhancement

Human Motion Transfer from Poses in the Wild

no code implementations7 Apr 2020 Jian Ren, Menglei Chai, Sergey Tulyakov, Chen Fang, Xiaohui Shen, Jianchao Yang

In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video.

Translation

EnlightenGAN: Deep Light Enhancement without Paired Supervision

8 code implementations17 Jun 2019 Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?

Image Restoration Low-Light Image Enhancement

Multimodal Style Transfer via Graph Cuts

2 code implementations ICCV 2019 Yulun Zhang, Chen Fang, Yilin Wang, Zhaowen Wang, Zhe Lin, Yun Fu, Jimei Yang

An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices.

Style Transfer

PaintBot: A Reinforcement Learning Approach for Natural Media Painting

no code implementations3 Apr 2019 Biao Jia, Chen Fang, Jonathan Brandt, Byungmoon Kim, Dinesh Manocha

Action selection is guided by a given reference image, which the agent attempts to replicate subject to the limitations of the action space and the agent's learned policy.

Curriculum Learning

Dance Dance Generation: Motion Transfer for Internet Videos

1 code implementation30 Mar 2019 Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, Tamara L. Berg

This work presents computational methods for transferring body movements from one person to another with videos collected in the wild.

Im2Pencil: Controllable Pencil Illustration from Photographs

1 code implementation CVPR 2019 Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang

We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.

Translation

A Multi-Agent-Based Rolling Optimization Method for Restoration Scheduling of Electrical Distribution Systems with Distributed Generation

no code implementations29 Dec 2018 Donghan Feng, Fan Wu, Yun Zhou, Usama Rahman, Xiaojin Zhao, Chen Fang

A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper.

Signal Processing

``Factual'' or ``Emotional'': Stylized Image Captioning with Adaptive Learning and Attention

no code implementations ECCV 2018 Tianlang Chen, Zhongping Zhang, Quanzeng You, Chen Fang, Zhaowen Wang, Hailin Jin, Jiebo Luo

It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context.

Image Captioning

Product Quantization Network for Fast Image Retrieval

no code implementations ECCV 2018 Tan Yu, Junsong Yuan, Chen Fang, Hailin Jin

Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features.

Image Retrieval Quantization

Flow-Grounded Spatial-Temporal Video Prediction from Still Images

1 code implementation ECCV 2018 Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang

Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame.

Video Prediction

"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention

no code implementations10 Jul 2018 Tianlang Chen, Zhongping Zhang, Quanzeng You, Chen Fang, Zhaowen Wang, Hailin Jin, Jiebo Luo

It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context.

Image Captioning

Learning from Multi-domain Artistic Images for Arbitrary Style Transfer

1 code implementation25 May 2018 Zheng Xu, Michael Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin

We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs.

Style Transfer

Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning

no code implementations ICLR 2018 Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa

Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language.

Representation Learning

Visual to Sound: Generating Natural Sound for Videos in the Wild

2 code implementations CVPR 2018 Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, Tamara L. Berg

As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world.

raw waveform

A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images

no code implementations2 Dec 2017 Chen Fang, Panuwat Janwattanapong, Chunfei Li, Malek Adjouadi

Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD.

General Classification

The Cultural Evolution of National Constitutions

no code implementations18 Nov 2017 Daniel N. Rockmore, Chen Fang, Nicholas J. Foti, Tom Ginsburg, David C. Krakauer

We explore how ideas from infectious disease and genetics can be used to uncover patterns of cultural inheritance and innovation in a corpus of 591 national constitutions spanning 1789 - 2008.

Visually-Aware Fashion Recommendation and Design with Generative Image Models

no code implementations7 Nov 2017 Wang-Cheng Kang, Chen Fang, Zhaowen Wang, Julian McAuley

Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i. e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features.

Recommendation Systems

Sketching With Style: Visual Search With Sketches and Aesthetic Context

no code implementations ICCV 2017 John Collomosse, Tu Bui, Michael J. Wilber, Chen Fang, Hailin Jin

We propose a novel measure of visual similarity for image retrieval that incorporates both structural and aesthetic (style) constraints.

Image Retrieval

Spatial-Semantic Image Search by Visual Feature Synthesis

no code implementations CVPR 2017 Long Mai, Hailin Jin, Zhe Lin, Chen Fang, Jonathan Brandt, Feng Liu

We train a convolutional neural network to synthesize appropriate visual features that captures the spatial-semantic constraints from the user canvas query.

Image Retrieval

Rethinking Skip-thought: A Neighborhood based Approach

no code implementations WS 2017 Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa

We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks.

General Classification

Trimming and Improving Skip-thought Vectors

no code implementations9 Jun 2017 Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa

The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics.

Text Classification

Universal Style Transfer via Feature Transforms

15 code implementations NeurIPS 2017 Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang

The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.

Image Reconstruction Style Transfer

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

no code implementations ICCV 2017 Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie

Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation.

Domain Adaptation

Diversified Texture Synthesis with Feed-forward Networks

no code implementations CVPR 2017 Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang

Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.

Texture Synthesis

Scribbler: Controlling Deep Image Synthesis with Sketch and Color

1 code implementation CVPR 2017 Patsorn Sangkloy, Jingwan Lu, Chen Fang, Fisher Yu, James Hays

In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces.

Colorization Image Generation

Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation

no code implementations15 Jul 2016 Ruining He, Chen Fang, Zhaowen Wang, Julian McAuley

Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences.

Recommendation Systems

Image Captioning with Semantic Attention

no code implementations CVPR 2016 Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, Jiebo Luo

Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing.

Image Captioning

Multi-Instance Visual-Semantic Embedding

no code implementations22 Dec 2015 Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, Alan Yuille

Visual-semantic embedding models have been recently proposed and shown to be effective for image classification and zero-shot learning, by mapping images into a continuous semantic label space.

General Classification Image Classification +1

Collaborative Feature Learning from Social Media

no code implementations CVPR 2015 Chen Fang, Hailin Jin, Jianchao Yang, Zhe Lin

We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities.

Multi-Task Metric Learning on Network Data

no code implementations10 Nov 2014 Chen Fang, Daniel N. Rockmore

In particular SPML learns a Mahalanobis distance metric for node attributes using network structure as supervision, so that the learned distance function encodes the structure and can be used to predict link patterns from attributes.

Metric Learning Multi-Task Learning

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