Search Results for author: Jun-Cheng Chen

Found 27 papers, 6 papers with code

Deep Complex U-Net with Conformer for Audio-Visual Speech Enhancement

no code implementations20 Sep 2023 Shafique Ahmed, Chia-Wei Chen, Wenze Ren, Chin-Jou Li, Ernie Chu, Jun-Cheng Chen, Amir Hussain, Hsin-Min Wang, Yu Tsao, Jen-Cheng Hou

Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems.

Speech Enhancement

MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance

no code implementations19 Aug 2023 Ernie Chu, Tzuhsuan Huang, Shuo-Yen Lin, Jun-Cheng Chen

This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow.

Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector

no code implementations16 Jul 2023 Shuo-Yen Lin, Ernie Chu, Che-Hsien Lin, Jun-Cheng Chen, Jia-Ching Wang

To the best of our knowledge, we are the first to propose DM-based naturalistic adversarial patch generation for object detectors.

Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models

no code implementations30 May 2023 Ernie Chu, Shuo-Yen Lin, Jun-Cheng Chen

To the best of our knowledge, our proposed method is the first to accomplish diverse and temporally consistent synthetic-to-real video translation using conditional image diffusion models.

Optical Flow Estimation Translation

DDPG based on multi-scale strokes for financial time series trading strategy

no code implementations5 Jun 2022 Jun-Cheng Chen, Cong-Xiao Chen, Li-Juan Duan, Zhi Cai

With the development of artificial intelligence, more and more financial practitioners apply deep reinforcement learning to financial trading strategies. However, It is difficult to extract accurate features due to the characteristics of considerable noise, highly non-stationary, and non-linearity of single-scale time series, which makes it hard to obtain high returns. In this paper, we extract a multi-scale feature matrix on multiple time scales of financial time series, according to the classic financial theory-Chan Theory, and put forward to an approach of multi-scale stroke deep deterministic policy gradient reinforcement learning model(MSSDDPG)to search for the optimal trading strategy. We carried out experiments on the datasets of the Dow Jones, S&P 500 of U. S. stocks, and China's CSI 300, SSE Composite, evaluate the performance of our approach compared with turtle trading strategy, Deep Q-learning(DQN)reinforcement learning strategy, and deep deterministic policy gradient (DDPG) reinforcement learning strategy. The result shows that our approach gets the best performance in China CSI 300, SSE Composite, and get an outstanding result in Dow Jones, S&P 500 of U. S.

Q-Learning reinforcement-learning +3

LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network

no code implementations29 Sep 2021 Nitin Balachandran, Jun-Cheng Chen, Rama Chellappa

Face super-resolution is a challenging and highly ill-posed problem since a low-resolution (LR) face image may correspond to multiple high-resolution (HR) ones during the hallucination process and cause a dramatic identity change for the final super-resolved results.

Face Hallucination Super-Resolution

Video-based Person Re-identification without Bells and Whistles

1 code implementation22 May 2021 Chih-Ting Liu, Jun-Cheng Chen, Chu-Song Chen, Shao-Yi Chien

Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS.

Video-Based Person Re-Identification

Class-Aware Robust Adversarial Training for Object Detection

no code implementations CVPR 2021 Pin-Chun Chen, Bo-Han Kung, Jun-Cheng Chen

Meanwhile, instead of normalizing the total loss with the number of objects, the proposed approach decomposes the total loss into class-wise losses and normalizes each class loss using the number of objects for the class.

Adversarial Robustness object-detection +1

Naturalistic Physical Adversarial Patch for Object Detectors

1 code implementation ICCV 2021 Yu-Chih-Tuan Hu, Bo-Han Kung, Daniel Stanley Tan, Jun-Cheng Chen, Kai-Lung Hua, Wen-Huang Cheng

Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches.

Pose And Joint-Aware Action Recognition

1 code implementation16 Oct 2020 Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava

Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition.

Action Classification Action Recognition In Videos +5

The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification

no code implementations ECCV 2020 Pirazh Khorramshahi, Neehar Peri, Jun-Cheng Chen, Rama Chellappa

In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information.

Vehicle Re-Identification

Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition

no code implementations ICCV 2019 Jingxiao Zheng, Ruichi Yu, Jun-Cheng Chen, Boyu Lu, Carlos D. Castillo, Rama Chellappa

In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph.

Face Recognition

An Automatic System for Unconstrained Video-Based Face Recognition

no code implementations10 Dec 2018 Jingxiao Zheng, Rajeev Ranjan, Ching-Hui Chen, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa

In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames.

Face Recognition

An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification

no code implementations16 Aug 2018 Boyu Lu, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa

In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance.

Face Recognition Face Verification

Deep Density Clustering of Unconstrained Faces

no code implementations CVPR 2018 Wei-An Lin, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa

In this paper, we consider the problem of grouping a collection of unconstrained face images in which the number of subjects is not known.


Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition

no code implementations3 Apr 2018 Rajeev Ranjan, Ankan Bansal, Hongyu Xu, Swami Sankaranarayanan, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa

We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.

Face Verification

A Proximity-Aware Hierarchical Clustering of Faces

no code implementations14 Mar 2017 Wei-An Lin, Jun-Cheng Chen, Rama Chellappa

In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations.

Clustering Face Clustering +1

Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

no code implementations15 Feb 2017 Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu, Jun-Cheng Chen, Carlos Castillo, Rama Chellappa

Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.

Face Recognition Face Verification

Deep Convolutional Neural Network Features and the Original Image

no code implementations6 Nov 2016 Connor J. Parde, Carlos Castillo, Matthew Q. Hill, Y. Ivette Colon, Swami Sankaranarayanan, Jun-Cheng Chen, Alice J. O'Toole

The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.

Face Recognition

Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks

no code implementations9 May 2016 Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Ching-Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa

Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.

Face Detection Face Recognition +3

Towards the Design of an End-to-End Automated System for Image and Video-based Recognition

no code implementations28 Jan 2016 Rama Chellappa, Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Vishal M. Patel, Carlos D. Castillo

In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.

Face Verification object-detection +2

Unconstrained Face Verification using Deep CNN Features

no code implementations7 Aug 2015 Jun-Cheng Chen, Vishal M. Patel, Rama Chellappa

In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset.

Face Verification

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