Search Results for author: Wei-Ting Chen

Found 20 papers, 9 papers with code

RobustSAM: Segment Anything Robustly on Degraded Images

no code implementations13 Jun 2024 Wei-Ting Chen, Yu-Jiet Vong, Sy-Yen Kuo, Sizhuo Ma, Jian Wang

Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation, acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system.

DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

no code implementations13 Jun 2024 Wei-Ting Chen, Gurunandan Krishnan, Qiang Gao, Sy-Yen Kuo, Sizhuo Ma, Jian Wang

Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images, which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks.

Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance

no code implementations17 May 2024 I-Hsiang Chen, Wei-Ting Chen, Yu-Wei Liu, Ming-Hsuan Yang, Sy-Yen Kuo

To address this issue, we introduce an effective approach to stabilize the proposal-target matching in point-based methods.

Crowd Counting

RobustSAM: Segment Anything Robustly on Degraded Images

no code implementations CVPR 2024 Wei-Ting Chen, Yu-Jiet Vong, Sy-Yen Kuo, Sizhou Ma, Jian Wang

Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system.

Deblurring Image Dehazing +6

DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

no code implementations CVPR 2024 Wei-Ting Chen, Gurunandan Krishnan, Qiang Gao, Sy-Yen Kuo, Sizhou Ma, Jian Wang

Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks.

Face Image Quality Face Image Quality Assessment +3

Counting Crowds in Bad Weather

no code implementations ICCV 2023 Zhi-Kai Huang, Wei-Ting Chen, Yuan-Chun Chiang, Sy-Yen Kuo, Ming-Hsuan Yang

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding.

Crowd Counting Image Restoration

DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields

no code implementations20 Mar 2023 Wei-Ting Chen, Wang Yifan, Sy-Yen Kuo, Gordon Wetzstein

Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction.

3D Shape Reconstruction Novel View Synthesis

RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning

1 code implementation18 Sep 2022 Wei-Ting Chen, I-Hsiang Chen, Chih-Yuan Yeh, Hao-Hsiang Yang, Hua-En Chang, Jian-Jiun Ding, Sy-Yen Kuo

Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision.

Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model

1 code implementation CVPR 2022 Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, Sy-Yen Kuo

At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem.

Transfer Learning

Multi-modal Bifurcated Network for Depth Guided Image Relighting

2 code implementations3 May 2021 Hao-Hsiang Yang, Wei-Ting Chen, Hao-Lun Luo, Sy-Yen Kuo

This model extracts the image and the depth features by the bifurcated network in the encoder.

Decoder Image Relighting +1

S3Net: A Single Stream Structure for Depth Guided Image Relighting

1 code implementation3 May 2021 Hao-Hsiang Yang, Wei-Ting Chen, and Sy-Yen Kuo

Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map.

Decoder Image Relighting +1

CREATe: Clinical Report Extraction and Annotation Technology

no code implementations28 Feb 2021 Yichao Zhou, Wei-Ting Chen, BoWen Zhang, David Lee, J. Harry Caufield, Kai-Wei Chang, Yizhou Sun, Peipei Ping, Wei Wang

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.

PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal

1 code implementation IEEE Transaction on Image Processing 2020 Wei-Ting Chen, Hao-Yu Feng, Jian-Jiun Ding, Sy-Yen Kuo

In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.

Computational Phenotyping Denoising +6

Assessing Graph-based Deep Learning Models for Predicting Flash Point

no code implementations26 Feb 2020 Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan

To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules.

PMS-Net: Robust Haze Removal Based on Patch Map for Single Images

1 code implementation CVPR 2019 Wei-Ting Chen, Jian-Jiun Ding, Sy-Yen Kuo

Conventional patch-based haze removal algorithms (e. g. the Dark Channel prior) usually performs dehazing with a fixed patch size.

Computational Phenotyping Denoising +6

Cannot find the paper you are looking for? You can Submit a new open access paper.