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Dynamic Graph Learning with Content-guided Spatial-Frequency Relation Reasoning for Deepfake Detection
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (43)
With the springing up of face synthesis techniques, it is prominent in need to develop powerful face forgery detection methods due to security concerns. Some existing methods attempt to employ auxiliary frequency-aware information combined with CNN backbones to discover the forged clues. Due to the inadequate information interaction with image content, the extracted frequency features are thus spa...Show More
Causality Network of Infectious Disease Revealed With Causal Decomposition
Jingpeng Sun;Kai Yuan;Chen Chen;Heng Xu;Hesong Wang;Yuxing Zhi;Silong Peng;Chung-Kang Peng;Norden Huang;Guangrui Huang;Albert Yang
Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here...Show More
This paper aims at robust and discriminative feature learning for target re-identification (Re-ID). In addition to paying attention to the individual appearance information as in most Re-ID methods, we further utilize the abundant contextual information as additional clues to guide the feature learning. Graph as a format of structured data is used to represent the target sample with its context. I...Show More
Navigating Diverse Salient Features for Vehicle Re-Identification
Mining sufficient discriminative information is vital for effective feature representation in vehicle re-identification. Traditional methods mainly focus on the most salient features and neglect whether the explored information is sufficient. This paper tackles the above limitation by proposing a novel Salience-Navigated Vehicle Re-identification Network (SVRN) which explores diverse salient featu...Show More
Label-Occurrence-Balanced Mixup for Long-Tailed Recognition
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Mixup is a popular data augmentation method, with many variants subsequently proposed. These methods mainly create new examples via convex combination of random data pairs and their corresponding one-hot labels. However, most of them adhere to a random sampling and mixing strategy, without considering the frequency of label occurrence in the mixing process. When applying mixup to long-tailed data,...Show More
CCANet: Exploiting Pixel-wise Semantics for Irregular Scene Text Spotting
2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Despite the progress in regular scene text spotting, how to detect and recognize irregular text with efficiency and accuracy remains a challenging task. In this work, we propose a novel Corner and Character Assisted Network (CCANet) which exploits pixel-wise semantics to learn explicit text corner and character center positions with low computational cost. Concretely, in the detection stage, we de...Show More
Deep Top-rank Counter Metric for Person Re-identification
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
In the research field of person re-identification, deep metric learning that guides the efficient and effective embedding learning serves as one of the most fundamental tasks. Recent efforts of the loss function based deep metric learning methods mainly focus on the top rank accuracy optimization by minimizing the distance difference between the correctly matching sample pair and wrongly matched s...Show More
Towards Low-Bit Quantization of Deep Neural Networks with Limited Data
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Recent machine learning methods use increasingly large deep neural networks to achieve state-of-the-art results in various tasks. Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of deep neural networks (DNNs) on cloud and edge devices. However, most of the existing methods usually need time-consuming trainin...Show More
Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification
Min Cao;Chen Chen;Hao Dou;Xiyuan Hu;Silong Peng;Arjan Kuijper
Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accura...Show More
Illuminating Vehicles With Motion Priors For Surveillance Vehicle Detection
2020 IEEE International Conference on Image Processing (ICIP)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Vehicle detection in traffic surveillance videos is a special subtask in object detection, where desired objects are vehicles moving on the road while the background is still within a sequence. The disparity of speed within each frame, i.e. moving and static, is consistent with the vehicle and background semantic to some extent, thus motions can be extracted to enhance the appearance of foreground...Show More
EvoQ: Mixed Precision Quantization of DNNs via Sensitivity Guided Evolutionary Search
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (19)
Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of deep neural networks (DNNs) on edge devices. However, most of the existing methods usually need time-consuming training or fine-tuning and access to the original training dataset that may be unavailable due to privacy or security concerns. In this paper, we ...Show More
The Equipment Nameplate Dataset for Scene Text Detection and Recognition
Xiaolong Chen;Zhengfu Zhang;Yu Qiao;Pu Zhang;Lanqing Guo;Wenrui Chen;Chen Chen;Bin Fu
2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and ...Show More
Boosting Local Shape Matching for Dense 3D Face Correspondence
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
Dense 3D face correspondence is a fundamental and challenging issue in the literature of 3D face analysis. Correspondence between two 3D faces can be viewed as a non-rigid registration problem that one deforms into the other, which is commonly guided by a few facial landmarks in many existing works. However, the current works seldom consider the problem of incoherent deformation caused by landmark...Show More
Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities
Md Habibur Rahman;Silong Peng;Xiyuan Hu;Chen Chen;Shahadat Uddin;Julian M. W. Quinn;Mohammad Ali Moni
Type 2 diabetes (T2D) is a chronic metabolic disorder characterised by high blood sugar and insulin insensitivity which greatly increases the risk of developing neurological diseases (NDs). The co-existence of T2D and comorbidities such as NDs can complicate or even cause the failure of standard treatments for those diseases. Comorbidities can be both causally linked and influence each other's dev...Show More
SnoreNet: Detecting Snore Events from Raw Sound Recordings
Jingpeng Sun;Xiyuan Hu;Yingying Zhao;Shuchen Sun;Chen Chen;Silong Peng
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
Snoring is one of the earliest symptoms of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). Snore detection is the first step in developing non-invasive, low-cost, and totally sound-based OSAHS analysis approaches. In this work, we propose a simple yet effective deep neural network, named SnoreNet, for detecting snores from a continuous sound recording. Without manually crafted features, SnoreNe...Show More
Asymmetric Cyclegan for Unpaired NIR-to-RGB Face Image Translation
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2019 | Conference Paper |
Cited by: Papers (14)
Translating near-infrared (NIR) face into color (RGB) face, is helpful to improve the visual effect of images and the performance of face recognition. The model for unpaired image-to-image translation is suitable for this task due to the high cost of pixel-matched data. Because of the complexity difference between NIR and RGB image domains, the complexity inequality in bidirectional NIR-RGB transl...Show More
Region-specific Metric Learning for Person Re-identification
2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Person re-identification addresses the problem of matching individual images of the same person captured by different non-overlapping camera views. Distance metric learning plays an effective role in addressing the problem. With the features extracted on several regions of person image, most of distance metric learning methods have been developed in which the learnt cross-view transformations are ...Show More
Apnea and Hypopnea Events Classification Using Amplitude Spectrum Trend Feature of Snores
Jingpeng Sun;Xiyuan Hu;Yingying Zhao;Shuchen Sun;Chen Chen;Silong Peng
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2018 | Conference Paper |
Research on snores for Obstructive Sleep Apnea Syndrome (OSAS) diagnosis is a new trend in recent years. In this paper, we proposed a snore-based apnea and hypopnea events classification approach. Firstly, we define the snores after the apnea event and during the hypopnea event as apnea-event-snore (AES) and hypopnea-event-snore (HES), respectively. Then, we design a new feature from the trend of ...Show More
From Groups to Co-Traveler Sets: Pair Matching Based Person Re-identification Framework
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Year: 2017 | Conference Paper |
Cited by: Papers (13)
In video surveillance, group refers to a set of people with similar velocity and close proximity. Group members can provide visual clues for person re-identification. In this paper, we discuss the essentials of group-based person re-identification and relax the group definition towards a concept of "co-traveler set", keeping constraints on velocity differences while loosening the distance constrai...Show More
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