Search Results for author: Ming-Syan Chen

Found 9 papers, 3 papers with code

In Anticipation of Perfect Deepfake: Identity-anchored Artifact-agnostic Detection under Rebalanced Deepfake Detection Protocol

no code implementations1 May 2024 Wei-Han Wang, Chin-Yuan Yeh, Hsi-Wen Chen, De-Nian Yang, Ming-Syan Chen

To bridge this gap, we introduce the Rebalanced Deepfake Detection Protocol (RDDP) to stress-test detectors under balanced scenarios where genuine and forged examples bear similar artifacts.

DeepFake Detection Face Swapping

Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning

no code implementations5 Sep 2023 Siyang Jiang, Rui Fang, Hsi-Wen Chen, Wei Ding, Ming-Syan Chen

The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task.

Few-Shot Learning

Overcoming Forgetting Catastrophe in Quantization-Aware Training

no code implementations ICCV 2023 Ting-An Chen, De-Nian Yang, Ming-Syan Chen

Afterward, we exploit replay data (a subset of old task data) for retraining in new tasks to alleviate the forgetting problem.

Quantization

PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift

1 code implementation8 May 2022 Siyang Jiang, Wei Ding, Hsi-Wen Chen, Ming-Syan Chen

Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set.

Data Augmentation Few-Shot Learning

AlignQ: Alignment Quantization With ADMM-Based Correlation Preservation

1 code implementation CVPR 2022 Ting-An Chen, De-Nian Yang, Ming-Syan Chen

Afterward, our theoretical analysis indicates that the significant changes in data correlations after the quantization induce a large quantization error.

Quantization

Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack

1 code implementation ICCV 2021 Chin-Yuan Yeh, Hsi-Wen Chen, Hong-Han Shuai, De-Nian Yang, Ming-Syan Chen

To improve efficiency, we introduce the limit-aware random gradient-free estimation and the gradient sliding mechanism to estimate the gradient that adheres to the adversarial limit, i. e., the pixel value limitations of the adversarial example.

Adversarial Attack Face Swapping +2

Video Event Detection by Inferring Temporal Instance Labels

no code implementations CVPR 2014 Kuan-Ting Lai, Felix X. Yu, Ming-Syan Chen, Shih-Fu Chang

To solve this problem, we propose a large-margin formulation which treats the instance labels as hidden latent variables, and simultaneously infers the instance labels as well as the instance-level classification model.

Event Detection

Sample-Specific Late Fusion for Visual Category Recognition

no code implementations CVPR 2013 Dong Liu, Kuan-Ting Lai, Guangnan Ye, Ming-Syan Chen, Shih-Fu Chang

However, the existing methods generally use a fixed fusion weight for all the scores of a classifier, and thus fail to optimally determine the fusion weight for the individual samples.

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