Search Results for author: Jia-Bin Huang

Found 65 papers, 41 papers with code

Shuffle and Attend: Video Domain Adaptation

no code implementations ECCV 2020 Jinwoo Choi, Gaurav Sharma, Samuel Schulter, Jia-Bin Huang

As the first novelty, we propose an attention mechanism which focuses on more discriminative clips and directly optimizes for video-level (cf.

Action Recognition Domain Adaptation

Learning Dynamic View Synthesis With Few RGBD Cameras

no code implementations22 Apr 2022 Shengze Wang, Youngjoong Kwon, Yuan Shen, Qian Zhang, Andrei State, Jia-Bin Huang, Henry Fuchs

Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency.

Frame Novel View Synthesis

Learning Neural Light Fields with Ray-Space Embedding Networks

1 code implementation2 Dec 2021 Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer, Johannes Kopf, Changil Kim

Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel.

Dynamic View Synthesis from Dynamic Monocular Video

no code implementations ICCV 2021 Chen Gao, Ayush Saraf, Johannes Kopf, Jia-Bin Huang

We present an algorithm for generating novel views at arbitrary viewpoints and any input time step given a monocular video of a dynamic scene.

DropLoss for Long-Tail Instance Segmentation

1 code implementation13 Apr 2021 Ting-I Hsieh, Esther Robb, Hwann-Tzong Chen, Jia-Bin Huang

Based on this insight, we develop DropLoss -- a novel adaptive loss to compensate for this imbalance without a trade-off between rare and frequent categories.

Instance Segmentation Object Detection +1

Learning Representational Invariances for Data-Efficient Action Recognition

1 code implementation30 Mar 2021 Yuliang Zou, Jinwoo Choi, Qitong Wang, Jia-Bin Huang

Data augmentation is a ubiquitous technique for improving image classification when labeled data is scarce.

Action Recognition Data Augmentation +1

Hybrid Neural Fusion for Full-frame Video Stabilization

2 code implementations ICCV 2021 Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang

Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.

Frame Video Stabilization

Portrait Neural Radiance Fields from a Single Image

no code implementations10 Dec 2020 Chen Gao, YiChang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang

We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait.


Robust Consistent Video Depth Estimation

1 code implementation CVPR 2021 Johannes Kopf, Xuejian Rong, Jia-Bin Huang

We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video.

Depth Estimation

Space-time Neural Irradiance Fields for Free-Viewpoint Video

no code implementations CVPR 2021 Wenqi Xian, Jia-Bin Huang, Johannes Kopf, Changil Kim

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video.

Depth Estimation

Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors

1 code implementation2 Nov 2020 Qi Mao, Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, Siwei Ma, Ming-Hsuan Yang

Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains.

Image-to-Image Translation Translation

Few-Shot Adaptation of Generative Adversarial Networks

1 code implementation22 Oct 2020 Esther Robb, Wen-Sheng Chu, Abhishek Kumar, Jia-Bin Huang

We validate our method in a challenging few-shot setting of 5-100 images in the target domain.

Image Generation

NAS-DIP: Learning Deep Image Prior with Neural Architecture Search

1 code implementation ECCV 2020 Yun-Chun Chen, Chen Gao, Esther Robb, Jia-Bin Huang

Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior for solving various inverse image restoration tasks.

Image Restoration Image-to-Image Translation +3

Semantic View Synthesis

1 code implementation ECCV 2020 Hsin-Ping Huang, Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang

We tackle a new problem of semantic view synthesis -- generating free-viewpoint rendering of a synthesized scene using a semantic label map as input.

Image Generation

Learning to See Through Obstructions with Layered Decomposition

1 code implementation11 Aug 2020 Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.

Optical Flow Estimation

FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

2 code implementations ECCV 2020 Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira

Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components.

Data Augmentation Semi-Supervised Image Classification

Instance-aware Image Colorization

2 code implementations CVPR 2020 Jheng-Wei Su, Hung-Kuo Chu, Jia-Bin Huang

Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly.


Consistent Video Depth Estimation

3 code implementations30 Apr 2020 Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, Johannes Kopf

We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video.

Depth Estimation

3D Photography using Context-aware Layered Depth Inpainting

1 code implementation CVPR 2020 Meng-Li Shih, Shih-Yang Su, Johannes Kopf, Jia-Bin Huang

We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.

Novel View Synthesis

Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

1 code implementation CVPR 2020 Yu-Lun Liu, Wei-Sheng Lai, Yu-Sheng Chen, Yi-Lung Kao, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang

We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.

HDR Reconstruction Quantization +1

Learning to See Through Obstructions

1 code implementation CVPR 2020 Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.

Optical Flow Estimation Reflection Removal

Deep Semantic Matching with Foreground Detection and Cycle-Consistency

no code implementations31 Mar 2020 Yun-Chun Chen, Po-Hsiang Huang, Li-Yu Yu, Jia-Bin Huang, Ming-Hsuan Yang, Yen-Yu Lin

Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations.

CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency

no code implementations CVPR 2019 Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang

Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e. g., synthetic to real images).

Data Augmentation Image-to-Image Translation +3

Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition

1 code implementation NeurIPS 2019 Jinwoo Choi, Chen Gao, Joseph C. E. Messou, Jia-Bin Huang

We validate the effectiveness of our method by transferring our pre-trained model to three different tasks, including action classification, temporal localization, and spatio-temporal action detection.

Action Classification Action Detection +4

Guided Image-to-Image Translation with Bi-Directional Feature Transformation

1 code implementation ICCV 2019 Badour AlBahar, Jia-Bin Huang

We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image.

Image-to-Image Translation Pose Transfer +1

Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation

1 code implementation13 Jun 2019 Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang

In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks.

Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification

no code implementations12 Jun 2019 Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira

We then show that when combined with these regularizers, the proposed method facilitates the propagation of information from generated prototypes to image data to further improve results.

General Classification Semi-Supervised Image Classification

DRIT++: Diverse Image-to-Image Translation via Disentangled Representations

4 code implementations2 May 2019 Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, Ming-Hsuan Yang

In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.

Image-to-Image Translation Perceptual Distance +1

Deep Paper Gestalt

2 code implementations20 Dec 2018 Jia-Bin Huang

Recent years have witnessed a significant increase in the number of paper submissions to computer vision conferences.

DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency

1 code implementation ECCV 2018 Yuliang Zou, Zelun Luo, Jia-Bin Huang

We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.

Depth And Camera Motion Optical Flow Estimation

VideoMatch: Matching based Video Object Segmentation

no code implementations ECCV 2018 Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing

Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

4 code implementations30 Aug 2018 Chen Gao, Yuliang Zou, Jia-Bin Huang

Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction.

Human-Object Interaction Detection

Learning Blind Video Temporal Consistency

1 code implementation ECCV 2018 Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, Ming-Hsuan Yang

Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.

Colorization Frame +4

DeepMVS: Learning Multi-view Stereopsis

1 code implementation CVPR 2018 Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, Jia-Bin Huang

We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction.

Progressive Representation Adaptation for Weakly Supervised Object Localization

1 code implementation12 Oct 2017 Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang

In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.

Classification General Classification +3

Joint Image Filtering with Deep Convolutional Networks

no code implementations11 Oct 2017 Yijun Li, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang

In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images.

Tracking Persons-of-Interest via Unsupervised Representation Adaptation

2 code implementations5 Oct 2017 Shun Zhang, Jia-Bin Huang, Jongwoo Lim, Yihong Gong, Jinjun Wang, Narendra Ahuja, Ming-Hsuan Yang

Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up.

Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks

7 code implementations4 Oct 2017 Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang

However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results.

Image Reconstruction Image Super-Resolution

Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight

1 code implementation2 Oct 2017 Yen-Chen Lin, Ming-Yu Liu, Min Sun, Jia-Bin Huang

Our core idea is that the adversarial examples targeting at a neural network-based policy are not effective for the frame prediction model.

Autonomous Vehicles Decision Making +2

Robust Visual Tracking via Hierarchical Convolutional Features

1 code implementation12 Jul 2017 Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang

Specifically, we learn adaptive correlation filters on the outputs from each convolutional layer to encode the target appearance.

Object Recognition Visual Tracking

Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking

1 code implementation7 Jul 2017 Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang

Second, we learn a correlation filter over a feature pyramid centered at the estimated target position for predicting scale changes.

Object Tracking

Removing Rain From Single Images via a Deep Detail Network

no code implementations CVPR 2017 Xueyang Fu, Jia-Bin Huang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN).

Denoising Rain Removal

Learning Structured Semantic Embeddings for Visual Recognition

no code implementations5 Jun 2017 Dong Li, Hsin-Ying Lee, Jia-Bin Huang, Shengjin Wang, Ming-Hsuan Yang

First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings.

General Classification Multi-Label Classification +2

Weakly Supervised Object Localization With Progressive Domain Adaptation

no code implementations CVPR 2016 Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang

In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.

Classification Domain Adaptation +4

A Comparative Study for Single Image Blind Deblurring

no code implementations CVPR 2016 Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, Ming-Hsuan Yang

Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms.

Single-Image Blind Deblurring

Detecting Migrating Birds at Night

no code implementations CVPR 2016 Jia-Bin Huang, Rich Caruana, Andrew Farnsworth, Steve Kelling, Narendra Ahuja

In this paper, we present a vision-based system for detecting migrating birds in flight at night.

Hierarchical Convolutional Features for Visual Tracking

no code implementations ICCV 2015 Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang

The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations.

Object Recognition Visual Object Tracking +1

Single Image Super-Resolution From Transformed Self-Exemplars

no code implementations CVPR 2015 Jia-Bin Huang, Abhishek Singh, Narendra Ahuja

However, the internal dictionary obtained from the given image may not always be sufficiently expressive to cover the textural appearance variations in the scene.

Image Super-Resolution

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