no code implementations • 4 Mar 2024 • Ying-Hsuan Wu, Jun-Wei Hsieh, Li Xin, Shin-You Teng, Yi-Kuan Hsieh, Ming-Ching Chang
In the second stage, our label refurbishment method is applied to obtain soft labels for multi-expert ensemble learning, providing a principled solution to the long-tail noisy label problem.
no code implementations • 20 Feb 2024 • Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields.
no code implementations • 16 Jan 2024 • Shang-Jui Kuo, Po-Han Huang, Chia-Ching Lin, Jeng-Lin Li, Ming-Ching Chang
Existing segmentation models relying on extensive annotations are impractical in real-world scenarios with limited annotated data.
no code implementations • 28 Dec 2023 • Yi-Kuan Hsieh, Jun-Wei Hsieh, Yu-Chee Tseng, Ming-Ching Chang, Li Xin
Furthermore, the use of a fixed Gaussian kernel fails to account for the varying pixel distribution with respect to the camera distance.
no code implementations • 23 Nov 2023 • Zhenfei Zhang, Mingyang Li, Ming-Ching Chang
Existing Image Manipulation Detection (IMD) methods are mainly based on detecting anomalous features arisen from image editing or double compression artifacts.
1 code implementation • 23 Aug 2023 • Yu-Xiang Zeng, Jun-Wei Hsieh, Xin Li, Ming-Ching Chang
Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors.
Ranked #1 on Scene Text Detection on SCUT-CTW1500
1 code implementation • 27 May 2023 • Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Erkhembayar Ganbold, Jun-Wei Hsieh, Ming-Ching Chang, Ping-Yang Chen, Byambaa Dorj, Hamad Al Jassmi, Ganzorig Batnasan, Fady Alnajjar, Mohammed Abduljabbar, Fang-Pang Lin
With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras.
Ranked #1 on 2D Object Detection on FishEye8K
no code implementations • 15 Apr 2023 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang, Rama Chellappa
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.
2 code implementations • 16 Nov 2022 • Yu-Hsiang Wang, Jun-Wei Hsieh, Ping-Yang Chen, Ming-Ching Chang, Hung Hin So, Xin Li
Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance.
Ranked #1 on Multi-Object Tracking on MOT20 (using extra training data)
no code implementations • 13 Nov 2022 • Yi-Kuan Hsieh, Jun-Wei Hsieh, Yu-Chee Tseng, Ming-Ching Chang, Bor-Shiun Wang
We then approximate the joint distribution of crowd density maps with the full covariance of multiple scales and derive a low-rank approximation for tractability and efficient implementation.
no code implementations • 3 Oct 2022 • Yi-Chun Wang, Jun-Wei Hsieh, Ming-Ching Chang
The first architecture search determines the inner cell structure, and the second architecture search considers exponentially growing paths to finalize the outer structure of the network.
no code implementations • 3 Oct 2022 • Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
We address this issue by generating secure data digests from the raw data and using them to guide model training at the FL moderator.
no code implementations • 3 Sep 2022 • Ying-Yu Chen, Jun-Wei Hsieh, Ming-Ching Chang
Few-Shot Learning (FSL) has attracted growing attention in computer vision due to its capability in model training without the need for excessive data.
Few-Shot Classification and Segmentation Few-Shot Image Classification +3
no code implementations • 13 May 2022 • Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, Siwei Lyu
In this work, we develop an online platform called Open-eye to study the human performance of AI-synthesized face detection.
2 code implementations • 21 Apr 2022 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Archana Venkatachalapathy, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Alice Li, Shangru Li, Rama Chellappa
The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries.
no code implementations • 11 Mar 2022 • Shu Hu, Chun-Hao Liu, Jayanta Dutta, Ming-Ching Chang, Siwei Lyu, Naveen Ramakrishnan
Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled.
no code implementations • 15 Feb 2022 • Xin Wang, Hui Guo, Shu Hu, Ming-Ching Chang, Siwei Lyu
Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts.
1 code implementation • 6 Dec 2021 • Ehab A. AlBadawy, Andrew Gibiansky, Qing He, JiLong Wu, Ming-Ching Chang, Siwei Lyu
We perform a subjective and objective evaluation to compare the performance of each vocoder along a different axis.
no code implementations • 13 Sep 2021 • Bor-Shiun Wang, Jun-Wei Hsieh, Ming-Ching Chang, Ping-Yang Chen, Lipeng Ke, Siwei Lyu
We introduce the Learning Discrete Wavelet Pooling (LDW-Pooling) that can be applied universally to replace standard pooling operations to better extract features with improved accuracy and efficiency.
no code implementations • 5 Sep 2021 • Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, Siwei Lyu
However, images from existing public datasets do not represent real-world scenarios well enough in terms of view variations and data distributions (where real faces largely outnumber synthetic faces).
no code implementations • 1 Sep 2021 • Hui Guo, Shu Hu, Xin Wang, Ming-Ching Chang, Siwei Lyu
Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones.
1 code implementation • 23 Aug 2021 • Jun-Wei Hsieh, Ming-Ching Chang, Ping-Yang Chen, Santanu Santra, Cheng-Han Chou, Chih-Sheng Huang
Our approach can improve bot the stability and accuracy of DARTS, by smoothing the loss landscape and sampling architecture parameters within a suitable bandwidth.
1 code implementation • 25 Apr 2021 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Yue Yao, Liang Zheng, Pranamesh Chakraborty, Christian E. Lopez, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff
Track 3 addressed city-scale multi-target multi-camera vehicle tracking.
1 code implementation • 3 Dec 2020 • Ping-Yang Chen, Ming-Ching Chang, Jun-Wei Hsieh, Yong-Sheng Chen
Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting.
Ranked #1 on Object Detection on UAVDT
no code implementations • 3 Oct 2020 • Yi Wei, Zhe Gan, Wenbo Li, Siwei Lyu, Ming-Ching Chang, Lei Zhang, Jianfeng Gao, Pengchuan Zhang
We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to guide the fine-grained image editing process.
no code implementations • 30 Apr 2020 • Milind Naphade, Shuo Wang, David Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Liang Zheng, Anuj Sharma, Rama Chellappa, Pranamesh Chakraborty
Track 3 addressed city-scale multi-target multi-camera vehicle tracking.
no code implementations • 16 Sep 2018 • Yuezun Li, Daniel Tian, Ming-Ching Chang, Xiao Bian, Siwei Lyu
Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms.
no code implementations • 16 Sep 2018 • Yuezun Li, Xiao Bian, Ming-Ching Chang, Siwei Lyu
In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object.
no code implementations • 5 Aug 2018 • Lipeng Ke, Ming-Ching Chang, Honggang Qi, Siwei Lyu
Human pose estimation is an important topic in computer vision with many applications including gesture and activity recognition.
no code implementations • 3 Jul 2018 • Wenbo Li, Ming-Ching Chang, Siwei Lyu
We present a bootstrapping framework to simultaneously improve multi-person tracking and activity recognition at individual, interaction and social group activity levels.
3 code implementations • 7 Jun 2018 • Yuezun Li, Ming-Ching Chang, Siwei Lyu
The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos.
no code implementations • ECCV 2018 • Lipeng Ke, Ming-Ching Chang, Honggang Qi, Siwei Lyu
We develop a robust multi-scale structure-aware neural network for human pose estimation.
Ranked #13 on Pose Estimation on MPII Human Pose
no code implementations • The IEEE International Conference on Computer Vision (ICCV), 2017 2017 • Wenbo Li, Longyin Wen, Ming-Ching Chang, Ser Nam Lim, Siwei Lyu
The RNNs in RNN-T are co-trained with the action category hierarchy, which determines the structure of RNN-T.
Ranked #106 on Skeleton Based Action Recognition on NTU RGB+D
no code implementations • 13 Nov 2015 • Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming-Ching Chang, Honggang Qi, Jongwoo Lim, Ming-Hsuan Yang, Siwei Lyu
In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.