Search Results for author: Yu-Chiang Frank Wang

Found 34 papers, 10 papers with code

LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity

1 code implementation CVPR 2021 Cheng-Fu Yang, Wan-Cyuan Fan, Fu-En Yang, Yu-Chiang Frank Wang

To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper.

LayoutTransformer: Relation-Aware Scene Layout Generation

no code implementations1 Jan 2021 Cheng-Fu Yang, Wan-Cyuan Fan, Fu-En Yang, Yu-Chiang Frank Wang

In the areas of machine learning and computer vision, text-to-image synthesis aims at producing image outputs given the input text.

Image Generation

Deep Representation Decomposition for Feature Disentanglement

no code implementations2 Nov 2020 Shang-Fu Chen, Jia-Wei Yan, Ya-Fan Su, Yu-Chiang Frank Wang

Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly.

Semantics-Guided Representation Learning with Applications to Visual Synthesis

no code implementations21 Oct 2020 Jia-Wei Yan, Ci-Siang Lin, Fu-En Yang, Yu-Jhe Li, Yu-Chiang Frank Wang

Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.

Representation Learning

Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning

no code implementations19 Oct 2020 Ci-Siang Lin, Yuan-Chia Cheng, Yu-Chiang Frank Wang

That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data.

Domain Generalization Generalizable Person Re-identification +1

Learning to Learn in a Semi-Supervised Fashion

no code implementations ECCV 2020 Yun-Chun Chen, Chao-Te Chou, Yu-Chiang Frank Wang

To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme.

Image Retrieval Meta-Learning +2

Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining

no code implementations17 Jul 2020 Hao-Hsiang Yang, Chao-Han Huck Yang, Yu-Chiang Frank Wang

Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales.

Single Image Deraining

Transforming Multi-Concept Attention into Video Summarization

no code implementations2 Jun 2020 Yen-Ting Liu, Yu-Jhe Li, Yu-Chiang Frank Wang

Video summarization is among challenging tasks in computer vision, which aims at identifying highlight frames or shots over a lengthy video input.

Video Summarization

Learning Resolution-Invariant Deep Representations for Person Re-Identification

no code implementations25 Jul 2019 Yun-Chun Chen, Yu-Jhe Li, Xiaofei Du, Yu-Chiang Frank Wang

Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.

Image Super-Resolution Person Re-Identification

Dual-modality seq2seq network for audio-visual event localization

1 code implementation20 Feb 2019 Yan-Bo Lin, Yu-Jhe Li, Yu-Chiang Frank Wang

Audio-visual event localization requires one to identify theevent which is both visible and audible in a video (eitherat a frame or video level).

audio-visual event localization

3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency

no code implementations29 Nov 2018 Yi-Lun Liao, Yao-Cheng Yang, Yu-Chiang Frank Wang

Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has drawn huge attention from researchers in computer vision and deep learning communities.

3D Reconstruction 3D Shape Reconstruction From A Single 2D Image

A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation

1 code implementation NeurIPS 2018 Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh, Yu-Chiang Frank Wang

We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains.

Unsupervised Domain Adaptation

Summarizing First-Person Videos from Third Persons' Points of View

no code implementations ECCV 2018 Hsuan-I Ho, Wei-Chen Chiu, Yu-Chiang Frank Wang

Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage.

Deep Generative Models for Weakly-Supervised Multi-Label Classification

no code implementations ECCV 2018 Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang

In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data.

Classification General Classification +1

Summarizing First-Person Videos from Third Persons' Points of Views

no code implementations ECCV 2018 Hsuan-I Ho, Wei-Chen Chiu, Yu-Chiang Frank Wang

Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage.

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

1 code implementation CVPR 2018 Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.

General Classification Knowledge Graphs +3

Order-Free RNN with Visual Attention for Multi-Label Classification

1 code implementation18 Jul 2017 Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang

In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification.

Classification General Classification +2

Learning Deep Latent Spaces for Multi-Label Classification

1 code implementation3 Jul 2017 Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank Wang

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance.

Classification General Classification +1

Generative-Discriminative Variational Model for Visual Recognition

no code implementations7 Jun 2017 Chih-Kuan Yeh, Yao-Hung Hubert Tsai, Yu-Chiang Frank Wang

In other words, our GDVM casts the supervised learning task as a generative learning process, with data discrimination to be jointly exploited for improved classification.

Classification General Classification +3

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

no code implementations CVPR 2018 Yen-Cheng Liu, Yu-Ying Yeh, Tzu-Chien Fu, Sheng-De Wang, Wei-Chen Chiu, Yu-Chiang Frank Wang

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated.

Representation Learning Unsupervised Domain Adaptation

No More Discrimination: Cross City Adaptation of Road Scene Segmenters

9 code implementations ICCV 2017 Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, Min Sun

Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases.

Semantic Segmentation

Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation

no code implementations CVPR 2016 Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank Wang

With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation.

Domain Adaptation

Propagated Image Filtering

no code implementations CVPR 2015 Jen-Hao Rick Chang, Yu-Chiang Frank Wang

In this paper, we propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions.

Image Denoising

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