Search Results for author: Yen-Yu Lin

Found 36 papers, 12 papers with code

Stripformer: Strip Transformer for Fast Image Deblurring

no code implementations10 Apr 2022 Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin, Chung-Chi Tsai, Chia-Wen Lin

Images taken in dynamic scenes may contain unwanted motion blur, which significantly degrades visual quality.

Deblurring Image Deblurring

An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation

no code implementations CVPR 2022 Cheng-Kun Yang, Ji-Jia Wu, Kai-Syun Chen, Yung-Yu Chuang, Yen-Yu Lin

We address weakly supervised point cloud segmentation by proposing a new model, MIL-derived transformer, to mine additional supervisory signals.

Multiple Instance Learning Point Cloud Segmentation

Unsupervised Point Cloud Object Co-Segmentation by Co-Contrastive Learning and Mutual Attention Sampling

1 code implementation ICCV 2021 Cheng-Kun Yang, Yung-Yu Chuang, Yen-Yu Lin

We formulate this task as an object point sampling problem, and develop two techniques, the mutual attention module and co-contrastive learning, to enable it.

Contrastive Learning

DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation

no code implementations6 Dec 2020 Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin, Wei Fan, Xiaohui Xie

Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images.

3D Hand Pose Estimation

Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos

no code implementations6 Dec 2020 Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin, Xiaohui Xie

Experiments show that our modelachieves surprisingly good results, with 3D estimation ac-curacy on par with the state-of-the-art models trained with3D annotations, highlighting the benefit of the temporalconsistency in constraining 3D prediction models.

Pose Estimation Self-Supervised Learning

MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation

no code implementations6 Dec 2020 Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin, Xiaohui Xie

Based on the match algorithm, we propose an efficient pipeline to generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D hand mesh and joint labels.

3D Hand Pose Estimation

Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector

1 code implementation ECCV 2020 Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, Ming-Hsuan Yang

A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds.

Domain Adaptation

Regularizing Meta-Learning via Gradient Dropout

1 code implementation13 Apr 2020 Hung-Yu Tseng, Yi-Wen Chen, Yi-Hsuan Tsai, Sifei Liu, Yen-Yu Lin, Ming-Hsuan Yang

With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization.

Domain Generalization Meta-Learning

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

Referring Expression Object Segmentation with Caption-Aware Consistency

1 code implementation10 Oct 2019 Yi-Wen Chen, Yi-Hsuan Tsai, Tiantian Wang, Yen-Yu Lin, Ming-Hsuan Yang

To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains.

Referring Expression Referring Expression Segmentation +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.

Unseen Object Segmentation in Videos via Transferable Representations

no code implementations8 Jan 2019 Yi-Wen Chen, Yi-Hsuan Tsai, Chu-Ya Yang, Yen-Yu Lin, Ming-Hsuan Yang

The entire process is decomposed into two tasks: 1) solving a submodular function for selecting object-like segments, and 2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video.

Semantic Segmentation

Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization

no code implementations ECCV 2018 Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Xiaoning Qian, Yung-Yu Chuang

In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN).

Co-Salient Object Detection

DeepCD: Learning Deep Complementary Descriptors for Patch Representations

1 code implementation ICCV 2017 Tsun-Yi Yang, Jo-Han Hsu, Yen-Yu Lin, Yung-Yu Chuang

This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for a patch by employing deep learning techniques.

Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales

1 code implementation CVPR 2016 Tsun-Yi Yang, Yen-Yu Lin, Yung-Yu Chuang

Experiments on popular benchmarks demonstrate the effectiveness of our descriptors and their superiority to the state-of-the-art descriptors.

Robust Image Alignment With Multiple Feature Descriptors and Matching-Guided Neighborhoods

no code implementations CVPR 2015 Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang

First, the performance of descriptor-based approaches to image alignment relies on the chosen descriptor, but the optimal descriptor typically varies from image to image, or even pixel to pixel.

Descriptor Ensemble: An Unsupervised Approach to Descriptor Fusion in the Homography Space

no code implementations13 Dec 2014 Yuan-Ting Hu, Yen-Yu Lin, Hsin-Yi Chen, Kuang-Jui Hsu, Bing-Yu Chen

Inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain, our approach stands on the unsupervised nature of feature matching, and can select a good descriptor for matching each feature point.

Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data

no code implementations CVPR 2014 Feng-Ju Chang, Yen-Yu Lin, Kuang-Jui Hsu

By treating a bounding box as a bag with its segment hypotheses as structured instances, MSIL-CRF selects the most likely segment hypotheses by leveraging the knowledge derived from both the labeled and uncertain training data.

Multiple Instance Learning Semantic Segmentation

Robust Feature Matching with Alternate Hough and Inverted Hough Transforms

no code implementations CVPR 2013 Hsin-Yi Chen, Yen-Yu Lin, Bing-Yu Chen

Inspired by the fact that nearby features on the same object share coherent homographies in matching, we cast the task of feature matching as a density estimation problem in the Hough space spanned by the hypotheses of homographies.

Density Estimation

Dimensionality Reduction for Data in Multiple Feature Representations

no code implementations NeurIPS 2008 Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh

In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance.

Dimensionality Reduction Graph Embedding +1

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