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- Ting-Cheng ChangSuggested from co-authorship
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Works148 from public data
- Deep Fake Image Detection Based on Pairwise Learning301
This paper proposes a deep learning-based approach for detecting the fake images by using the contrastive loss and demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.
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- Learning to Detect Fake Face Images in the Wild145
A deep forgery discriminator (DeepFD) to efficiently and effectively detect the computer-generated images and adopt contrastive loss in seeking the typical features of the synthesized images generated by different GANs and follow by concatenating a classifier to detect such computer- generated images.
- CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data137
The proposed convolutional neural network to jointly solve clustering and representation learning in an iterative manner outperforms start-of-the-art clustering schemes in terms of accuracy and storage complexity on large-scale image sets containing millions of images.
- DRCT: Saving Image Super-Resolution away from Information Bottleneck120
The Dense-residual-connected Transformer (DRCT) is proposed, aimed at mitigating the loss of spatial information and stabilizing the information flow through dense-residual connections between layers, thereby unleashing the model’s potential and saving the model away from information bottleneck.
- AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results110
The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.
- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
- SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination90
A Siamese GAN to reconstruct HR faces that visually resemble their corresponding identities that significantly outperforms existing face hallucination GANs in objective face verification performance while achieving promising visual-quality reconstruction.
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- NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results83
This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained, and offers a comprehensive overview of current trends in this field.
- ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images83
The ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models in the Automatic Detection challenge on Age-related Macular degeneration (ADAM).
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- Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report70
The target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs.
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- 1 st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results48
A new benchmark is introduced, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage and is summarized in the report.
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- Popularity Prediction of Social Media based on Multi-Modal Feature Mining32
A popularity prediction framework for social media based on multi-modal feature mining and an ensemble regression approach is presented that significantly outperforms other state-of-the-art regression models and could effectively improve the performance in the social media prediction challenge.
- Deep learning-based vehicle trajectory prediction based on generative adversarial network for autonomous driving applications31
A GAN (generative adversarial network)-based deep learning framework is presented in this paper for predicting the trajectories of surrounding vehicles of an autonomous vehicle in an RGB image sequence without explicit coordinate annotation to solve the main problem addressed.
- Social Media Prediction Based on Residual Learning and Random Forest29
A unified framework for the residual learning and random forest regression for social media prediction task and shows that the performance of the proposed method significantly outperforms other methods.
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- Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?28
It is demonstrated that it is important to use preprocessing to highlight the image features and further give these features for deep learning models, in this way, the classification accuracy will be better to just use the original images forDeep learning.
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- NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results26
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes and offers an in-depth overview of the latest trends in the field.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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