Search Results for author:
Found 128 papers, 49 papers with code
Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation
Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.
HySurvPred: Multimodal Hyperbolic Embedding with Angle-Aware Hierarchical Contrastive Learning and Uncertainty Constraints for Survival Prediction
To better integrate multimodal features in hyperbolic space, we introduce the ARCL module, which uses ranking-based contrastive learning to preserve the ordinal nature of survival time, along with the CUC module to fully explore the censored data.
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification
In this work, we present MedKAN, a medical image classification framework built upon KAN and its convolutional extensions.
Bringing RGB and IR Together: Hierarchical Multi-Modal Enhancement for Robust Transmission Line Detection
Ensuring a stable power supply in rural areas relies heavily on effective inspection of power equipment, particularly transmission lines (TLs).
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification
Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance.
MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
Finally, we assess the utility of MedCoDi-M in addressing key challenges in the medical field, such as anonymization, data scarcity and imbalance learning.
DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis
To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs.
Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection
In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces.
BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks.
DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching
In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression.
{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation
Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention.
CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing
For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions.
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image
Recent advancements in computational pathology have produced patch-level Multi-modal Large Language Models (MLLMs), but these models are limited by their inability to analyze whole slide images (WSIs) comprehensively and their tendency to bypass crucial morphological features that pathologists rely on for diagnosis.
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking
To address this gap, we present MedChain, a dataset of 12, 163 clinical cases that covers five key stages of clinical workflow.
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion
Additionally, we train a warping module to align the hair color with the target region.
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain.
SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data
Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection.
Low-Light Enhancement Effect on Classification and Detection: An Empirical Study
Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively.
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency
Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA).
MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection
To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD).
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation
While large visual models (LVM) demonstrated significant potential in image understanding, due to the application of large-scale pre-training, the Segment Anything Model (SAM) has also achieved great success in the field of image segmentation, supporting flexible interactive cues and strong learning capabilities.
Activation Space Selectable Kolmogorov-Arnold Networks
To address this issue, we propose an activation space Selectable KAN (S-KAN).
GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph
To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process.
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical role in precision machining and high-end equipment manufacturing.
WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
Deep image registration has demonstrated exceptional accuracy and fast inference.
GM-DF: Generalized Multi-Scenario Deepfake Detection
In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets.
SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis
To enhance the encoder's ability to capture fine-grained features, we incorporate the image encoder of SAM to construct a texture consistency (TC) loss, which extracts image embedding from the input and output of reconstruction branch, and then enforces both embedding into the same feature space.
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples.
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network
Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone.
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim.
EPL: Empirical Prototype Learning for Deep Face Recognition
When the prototypes are updated using the facial sample feature gradients in the model training, they are prone to being pulled away from the class center by the hard samples, resulting in decreased overall model performance.
Dynamic data sampler for cross-language transfer learning in large language models
Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications.
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions.
Ranked #3 on
Facial Action Unit Detection
on BP4D
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Low-light image enhancement (LLIE) aims to improve low-illumination images.
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
Rediscovering BCE Loss for Uniform Classification
We also propose the uniform classification accuracy as a metric to measure the model's performance in uniform classification.
Fingerprint Presentation Attack Detector Using Global-Local Model
While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained.
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support.
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems.
GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators.
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels.
Ranked #7 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Tune-An-Ellipse: CLIP Has Potential to Find What You Want
Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities.
SCPMan: Shape Context and Prior Constrained Multi-scale Attention Network for Pancreatic Segmentation
Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries.
Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation
To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process.
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning
We suggest that the proposed method may contribute to the advancement of data-driven self-supervised learning research, bringing a fresh perspective to this community.
TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction
In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) which enables the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes.
High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
In this study, the structural problems of the YOLOv5 model were analyzed emphatically.
MRecGen: Multimodal Appropriate Reaction Generator
Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour.
TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction
When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available.
ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions
To address these limitations, this paper reformulates the task as an extrapolation or prediction problem, and proposes an novel framework (called ReactFace) to generate multiple different but appropriate facial reactions from a speaker behaviour rather than merely replicating the corresponding listener facial behaviours.
VisorGPT: Learning Visual Prior via Generative Pre-Training
Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.
Two-Stream Regression Network for Dental Implant Position Prediction
In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue.
YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification
Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1. 1%, 2. 3%, and 5. 2%, respectively.
Open-World Weakly-Supervised Object Localization
To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to generate a complete G-CAM (Generalized Class Activation Map) for object localization, without the requirement of bounding box annotation.
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection
This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW).
Precise Facial Landmark Detection by Reference Heatmap Transformer
Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results.
StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis
As cycle-like losses are designed to measure the L_2 distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i. e. no paired kinship face data is required.
Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition
Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i. e., they make predictions mainly based on texture cues.
UniFace: Unified Cross-Entropy Loss for Deep Face Recognition
To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation
The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data.
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation
Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based image classifier.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation
Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity.
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator
\noindent Traditional L_p norm-restricted image attack algorithms suffer from poor transferability to black box scenarios and poor robustness to defense algorithms.
Sample hardness based gradient loss for long-tailed cervical cell detection
Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution.
Activation Template Matching Loss for Explainable Face Recognition
Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets?
Selective Multi-Scale Learning for Object Detection
When integrated with SMSL, two-stage detectors can get around 1. 0\% improvement in AP.
Delving into the Scale Variance Problem in Object Detection
For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2. 5\% improvement in AP (on COCO 2017 dataset), with only 3\% increase of FLOPs.
Robust Representation via Dynamic Feature Aggregation
With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.
Scene Consistency Representation Learning for Video Scene Segmentation
Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods.
Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display.
Ranked #7 on
Facial Action Unit Detection
on DISFA
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations.
Cross Language Image Matching for Weakly Supervised Semantic Segmentation
As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
FEAT: Face Editing with Attention
In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation
As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
Gated SwitchGAN for multi-domain facial image translation
The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation.
Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising
The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels.
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition
This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours.
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization
In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner.
Manifold-preserved GANs
Generative Adversarial Networks (GANs) have been widely adopted in various fields.
Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing
Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification
We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet).
Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis
The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data.
Self-Attention Based Text Knowledge Mining for Text Detection
Considering the importance of exploring text contents for text detection, we propose STKM (Self-attention based Text Knowledge Mining), which consists of a CNN Encoder and a Self-attention Decoder, to learn general prior knowledge for text detection from SynthText.
Neuron segmentation using 3D wavelet integrated encoder-decoder network
Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers.
Learning a Model-Driven Variational Network for Deformable Image Registration
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
Ranked #7 on
6D Pose Estimation using RGBD
on REAL275
Group-wise Inhibition based Feature Regularization for Robust Classification
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).
Ranked #40 on
Domain Generalization
on ImageNet-C
MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face.
GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing
Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing.
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0. 79457, 0. 87494, and 0. 83712, respectively.
Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network
Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.
Context Aware 3D UNet for Brain Tumor Segmentation
UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation.
Think about boundary: Fusing multi-level boundary information for landmark heatmap regression
Although current face alignment algorithms have obtained pretty good performances at predicting the location of facial landmarks, huge challenges remain for faces with severe occlusion and large pose variations, etc.
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.
Translate the Facial Regions You Like Using Region-Wise Normalization
We propose in this paper a region-wise normalization framework, for region level face translation.
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.
Instance-aware Self-supervised Learning for Nuclei Segmentation
Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.
Geometry Constrained Weakly Supervised Object Localization
The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.
WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation
In deep networks, the lost data details significantly degrade the performances of image segmentation.
Wavelet Integrated CNNs for Noise-Robust Image Classification
The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets.
Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks
It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples.
Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training
We present a new method for improving the performances of variational autoencoder (VAE).
Inferring Dynamic Representations of Facial Actions from a Still Image
We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation.
Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification
In this paper, we present a novel deep hybrid attention approach to breast cancer classification.
Texture Deformation Based Generative Adversarial Networks for Face Editing
We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Self-Organizing Tensor Architecture for Multi-View Clustering
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification
However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e. g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas.
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification
The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise.
Active Learning for Breast Cancer Identification
Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images.
Kernelized Support Tensor Machines
In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.
Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.
Skin Lesion Classification using Class Activation Map
We proposed a two stage framework with only one network to analyze skin lesion images, we firstly trained a convolutional network to classify these images, and cropped the import regions which the network has the maximum activation value.
Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network
In this paper, we proposed two deep learning methods to address all the three tasks announced in ISIC 2017, i. e. lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers.
Deep Feature Consistent Variational Autoencoder
We present a novel method for constructing Variational Autoencoder (VAE).
Object Specific Deep Learning Feature and Its Application to Face Detection
We present a method for discovering and exploiting object specific deep learning features and use face detection as a case study.
Latent Constrained Correlation Filters for Object Localization
There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.
LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification
It is worth to mention that we achieve a 65. 4\% classification accuracy-- which is, to the best of our knowledge, the highest record by far --on Flickr Material Database by using a single feature.
The BeiHang Keystroke Dynamics Authentication System
Keystroke Dynamics is an important biometric solution for person authentication.
