Search Results for author:
Found 214 papers, 82 papers with code
Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering
However, the existing clustering methods based on language similarity cannot handle the asymmetric problem in multilingual NMT, i. e., one translation task A can benefit from another translation task B but task B will be harmed by task A.
Assessing Judging Bias in Large Reasoning Models: An Empirical Study
Large Reasoning Models (LRMs) like DeepSeek-R1 and OpenAI-o1 have demonstrated remarkable reasoning capabilities, raising important questions about their biases in LLM-as-a-judge settings.
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR).
EditCLIP: Representation Learning for Image Editing
We introduce EditCLIP, a novel representation-learning approach for image editing.
High-Quality Spatial Reconstruction and Orthoimage Generation Using Efficient 2D Gaussian Splatting
Highly accurate geometric precision and dense image features characterize True Digital Orthophoto Maps (TDOMs), which are in great demand for applications such as urban planning, infrastructure management, and environmental monitoring.
Pathology Image Restoration via Mixture of Prompts
In this work, we devise a two-stage restoration solution cascading a transformer and a diffusion model, to benefit from their powers in preserving image fidelity and perceptual quality, respectively.
LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening
The deployment of computer-aided diagnosis systems for cervical cancer screening using whole slide images (WSIs) faces critical challenges due to domain shifts caused by staining variations across different scanners and imaging environments.
Med-LEGO: Editing and Adapting toward Generalist Medical Image Diagnosis
The adoption of visual foundation models has become a common practice in computer-aided diagnosis (CAD).
MITracker: Multi-View Integration for Visual Object Tracking
Multi-view object tracking (MVOT) offers promising solutions to challenges such as occlusion and target loss, which are common in traditional single-view tracking.
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?
In this blog, we present a brief review of recent advancements in LLMs related to retrieval-augmented generation, multi-step reasoning, external tools, and computational expressivity, all of which substantially enhance LLM performance.
X-IL: Exploring the Design Space of Imitation Learning Policies
Designing modern imitation learning (IL) policies requires making numerous decisions, including the selection of feature encoding, architecture, policy representation, and more.
NatureLM: Deciphering the Language of Nature for Scientific Discovery
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages.
Semantic Communication with Entropy-and-Channel-Adaptive Rate Control
Traditional wireless image transmission methods struggle to balance rate efficiency and reconstruction quality under varying channel conditions.
What Limits LLM-based Human Simulation: LLMs or Our Design?
We argue that advancing LLM-based human simulation requires addressing both LLM's inherent limitations and simulation framework design challenges.
ARMOR: Shielding Unlearnable Examples against Data Augmentation
To address this issue, we propose a defense framework, dubbed ARMOR, to protect data privacy from potential breaches of data augmentation.
A Survey on Facial Image Privacy Preservation in Cloud-Based Services
Facial recognition models are increasingly employed by commercial enterprises, government agencies, and cloud service providers for identity verification, consumer services, and surveillance.
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL
Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems.
Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies
Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its structural and functional changes.
OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation
As virtual reality gains popularity, the demand for controllable creation of immersive and dynamic omnidirectional videos (ODVs) is increasing.
An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers
Our proposed backdoor attack framework also showcases robustness against state-of-the-art backdoor defenses.
Megatron: Evasive Clean-Label Backdoor Attacks against Vision Transformer
To generate an effective trigger, we customize two loss terms based on the attention mechanism used in transformer networks, i. e., latent loss and attention diffusion loss.
Hidden Data Privacy Breaches in Federated Learning
Federated Learning (FL) emerged as a paradigm for conducting machine learning across broad and decentralized datasets, promising enhanced privacy by obviating the need for direct data sharing.
StereoCrafter-Zero: Zero-Shot Stereo Video Generation with Noisy Restart
Generating high-quality stereo videos that mimic human binocular vision requires maintaining consistent depth perception and temporal coherence across frames.
ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization
They empirically inject a watermark that is both invisible and robust and passively achieve concealment by limiting the strength of the watermark, thus reducing the robustness.
Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent
In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens.
ZIF-90 treats fungal keratitis by promoting macrophage apoptosis and inhibiting inflammatory response
In addition, to investigate whether the metal-ligand zinc and the organic ligand imidazole act as essential factors in ZIF-90, we investigated the in vitro antimicrobial and anti-inflammatory effects of ZIF-8, ZIF-67, and MOF-74 (Zn) by MIC and ELISA experiments.
Age of Information-Oriented Probabilistic Link Scheduling for Device-to-Device Networks
However, finding the optimal policy for the formulated MDP in its original form is challenging due to the intertwined AoI dynamics of all D2D links.
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI Segmentation
High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various medical image analysis tasks.
Zeolitic Imidazolate Framework-8 offers an anti-inflammatory and antifungal method in the treatment of Aspergillus fungus keratitis in vitro and in vivo
Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability.
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI
By using a denoising variational autoencoder, our proposed pipeline further compresses the connectivity features into 5 latent Gaussian distributions, providing is a low-dimensional representation of the data to promote computational efficiency and interpretability.
Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation
Here, we present TUMSyn, a Text-guided Universal MR image Synthesis generalist model, which can flexibly generate brain MR images with demanded imaging metadata from routinely acquired scans guided by text prompts.
Revisiting Video Quality Assessment from the Perspective of Generalization
We begin by analyzing the weight loss landscape of VQA models, identifying a strong correlation between this landscape and the generalization gaps.
PAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMs
Large Language Models (LLMs) have excelled in various tasks but are still vulnerable to jailbreaking attacks, where attackers create jailbreak prompts to mislead the model to produce harmful or offensive content.
Advancing Video Quality Assessment for AIGC
In recent years, AI generative models have made remarkable progress across various domains, including text generation, image generation, and video generation.
ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification
The misuse of deep learning-based facial manipulation poses a potential threat to civil rights.
One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
Current Pose-Guided Person Image Synthesis (PGPIS) methods depend heavily on large amounts of labeled triplet data to train the generator in a supervised manner.
OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model
With the same reconstruction quality, the more sufficient the VAE's compression for videos is, the more efficient the LVDMs are.
DCIM-AVSR : Efficient Audio-Visual Speech Recognition via Dual Conformer Interaction Module
Speech recognition is the technology that enables machines to interpret and process human speech, converting spoken language into text or commands.
Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers
Based on the observations from such an extensive empirical study, we propose a novel approach to cough-based disease classification based on both self-supervised and supervised learning on a large-scale cough data set.
Barbie: Text to Barbie-Style 3D Avatars
In this paper, we propose Barbie, a novel framework for generating 3D avatars that can be dressed in diverse and high-quality Barbie-like garments and accessories.
IDRetracor: Towards Visual Forensics Against Malicious Face Swapping
To achieve visual forensics and target face attribution, we propose a novel task named face retracing, which considers retracing the original target face from the given fake one via inverse mapping.
Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization
Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio.
Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 years old
The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization.
A Survey of Defenses against AI-generated Visual Media: Detection, Disruption, and Authentication
Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis.
A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions.
MaIL: Improving Imitation Learning with Mamba
MaIL leverages Mamba, a state-space model designed to selectively focus on key features of the data.
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning
To reduce slice spacing, deep-learning-based super-resolution techniques are widely investigated.
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving
Extensive evaluations demonstrate the superior performance of ControlLoc, achieving an impressive average attack success rate of around 98. 1% across various AD visual perceptions and datasets, which is four times greater effectiveness than the existing hijacking attack.
Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models
We introduce the first zero-shot approach for Video Semantic Segmentation (VSS) based on pre-trained diffusion models.
Enhancing Adversarial Transferability Through Neighborhood Conditional Sampling
Transfer-based attacks craft adversarial examples utilizing a white-box surrogate model to compromise various black-box target models, posing significant threats to many real-world applications.
Detecting Adversarial Data via Perturbation Forgery
By learning to distinguish this open covering from the distribution of natural data, we can develop a detector with strong generalization capabilities against all types of adversarial attacks.
Gaze-DETR: Using Expert Gaze to Reduce False Positives in Vulvovaginal Candidiasis Screening
Accurate detection of vulvovaginal candidiasis is critical for women's health, yet its sparse distribution and visually ambiguous characteristics pose significant challenges for accurate identification by pathologists and neural networks alike.
Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation
In this framework, each agent is specialized in a distinct aspect, such as foreground understanding, diversity enhancement, object integrity protection, and textual prompt consistency.
Deep Learning Library Testing: Definition, Methods and Challenges
This paper provides an overview of the testing research related to various DL libraries, discusses the strengths and weaknesses of existing methods, and provides guidance and reference for the application of the DL library.
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Improving Learnt Local MAPF Policies with Heuristic Search
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations.
NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
The rapid advancement of Large Language Models (LLMs) has introduced a new frontier in natural language processing, particularly in understanding and processing long-context information.
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval
Audio-text retrieval (ATR), which retrieves a relevant caption given an audio clip (A2T) and vice versa (T2A), has recently attracted much research attention.
Revisiting Adversarial Training under Long-Tailed Distributions
Extensive experiments further corroborate that data augmentation alone can significantly improve robustness.
MUC: Mixture of Uncalibrated Cameras for Robust 3D Human Body Reconstruction
Rather than merely averaging the models across views, we develop a neural network trained to assign weights to individual views for all human body joints, based on the estimated distribution of joint distances from each camera.
DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network Scenarios
In this paper, we propose a Robust Adversarial Perturbation (DF-RAP) that provides persistent protection for facial images under OSN compression.
Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation
In this paper, we propose a new data stream design paradigm, dubbed Sliver, that addresses the timeliness and accuracy problem of labels by reducing the window size and implementing a sliding window correspondingly.
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening
In the first Global Image Generation stage, a Normal Image Generator is designed to generate cytopathological images full of normal cervical cells.
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs).
Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces
Deepfake videos are becoming increasingly realistic, showing few tampering traces on facial areasthat vary between frames.
Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
In this paper, we propose to dynamically learn the weights of SSL tasks for different nodes and fuse the embeddings learned from different SSL tasks to boost performance.
360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model
In this paper, we propose a pipeline named 360-Degree Video Diffusion model (360DVD) for generating 360-degree panoramic videos based on the given prompts and motion conditions.
Enhancing the Performance of DeepReach on High-Dimensional Systems through Optimizing Activation Functions
Hamilton-Jacobi Reachability Analysis is a formal verification method that guarantees performance and safety for dynamical systems and is widely applicable to various tasks and challenges.
Continual Adversarial Defense
In response to the rapidly evolving nature of adversarial attacks against visual classifiers on a monthly basis, numerous defenses have been proposed to generalize against as many known attacks as possible.
CLIP in Medical Imaging: A Survey
With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications.
Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis
Accordingly, we introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks.
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images
Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI).
MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis
By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.
EX-Graph: A Pioneering Dataset Bridging Ethereum and X
While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data.
Large Language Model Soft Ideologization via AI-Self-Consciousness
Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks.
Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classification
Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization.
Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening
Finally, we use a contrastive learning method to alleviate the overfitting caused by the style gap of WSIs and suppress the attention to false positive regions.
AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation
To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes.
Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces
In the recovering stage, the model focuses on randomly masking regions of interest (ROIs) and reconstructing real faces without unpredictable tampered traces, resulting in a relatively good recovery effect for real faces while a poor recovery effect for fake faces.
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
In the face of climate change, are companies really taking substantial steps toward more sustainable operations?
Downstream-agnostic Adversarial Examples
AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder.
CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification
In this paper, we propose CellGAN to synthesize cytopathological images of various cervical cell types for augmenting patch-level cell classification.
Multi-Scenario Ranking with Adaptive Feature Learning
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.
Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations.
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach
Video scene parsing incorporates temporal information, which can enhance the consistency and accuracy of predictions compared to image scene parsing.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control
Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources.
InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions
In this work, we propose a framework termed InstructEdit that can do fine-grained editing based on user instructions.
ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs
The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor.
GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation
Local Transformer uses a dynamic graph to calculate all neighboring point weights by intra-domain cross-attention with dynamically updated graph relations, so that every neighboring point could affect the features of centroid with different weights; Global Transformer enlarges the receptive field of Local Transformer by a global self-attention.
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work.
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs
The model is built on a linear term that can make a reasonably accurate prediction and a nonlinear correction for accuracy improvement.
Learning Better Contrastive View from Radiologist's Gaze
Then, we propose a novel augmentation method, i. e., FocusContrast, to learn from radiologists' gaze in diagnosis and generate contrastive views for medical images with guidance from radiologists' visual attention.
Backdoor Attack with Sparse and Invisible Trigger
More importantly, it is not feasible to simply combine existing methods to design an effective sparse and invisible backdoor attack.
VTPNet for 3D deep learning on point cloud
Recently, Transformer-based methods for point cloud learning have achieved good results on various point cloud learning benchmarks.
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine.
Detecting Adversarial Faces Using Only Real Face Self-Perturbations
Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems.
Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion
Magnetic resonance (MR) images collected in 2D scanning protocols typically have large inter-slice spacing, resulting in high in-plane resolution but reduced through-plane resolution.
chatClimate: Grounding Conversational AI in Climate Science
The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high).
DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task
The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable.
Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data
Point cloud data are widely used in manufacturing applications for process inspection, modeling, monitoring and optimization.
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate.
MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path
Image generation using diffusion can be controlled in multiple ways.
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes
3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world.
Mover: Mask and Recovery based Facial Part Consistency Aware Method for Deepfake Video Detection
Specifically, given a real face image, we first pretrain a masked autoencoder to learn facial part consistency by dividing faces into three parts and randomly masking ROIs, which are then recovered based on the unmasked facial parts.
Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks
Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions.
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice.
RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches.
Implicit Identity Driven Deepfake Face Swapping Detection
Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake.
Panoptic Compositional Feature Field for Editable Scene Rendering With Network-Inferred Labels via Metric Learning
In this work, we attempt to learn an object-compositional neural implicit representation for editable scene rendering by leveraging labels inferred from the off-the-shelf 2D panoptic segmentation networks instead of the ground truth annotations.
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery
Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers.
On Fine-Tuned Deep Features for Unsupervised Domain Adaptation
Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptation task.
Whole Page Unbiased Learning to Rank
To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model with causal discovery and mitigate the biases induced by multiple SERP features with no specific design.
Probabilistic Generative Transformer Language models for Generative Design of Molecules
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction.
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation
To address these issues, we propose a novel medical image inpainting model named TBI-GAN to synthesize TBI MR scans with paired brain label maps.
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection
Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task.
Dynamic Budget Throttling in Repeated Second-Price Auctions
Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in.
DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing
Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution.
Deep Generalized Unfolding Networks for Image Restoration
Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation.
Ranked #5 on
Image Restoration
on CDD-11
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis
In this paper, we demonstrate that the eye movement of radiologists reading medical images can be a new form of supervision to train the DNN-based computer-aided diagnosis (CAD) system.
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level.
Automatic Facial Skin Feature Detection for Everyone
We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild.
Towards Benchmarking and Evaluating Deepfake Detection
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos.
Transformers in Medical Image Analysis: A Review
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area.
Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability.
Parameter Differentiation based Multilingual Neural Machine Translation
Further analyses reveal that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.
Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications.
Reconstructing Training Data from Diverse ML Models by Ensemble Inversion
This technique leads to noticeable improvements of the quality of the generated samples with distinguishable features of the dataset entities compared to MI of a single ML model.
Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation
The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes.
Anti-Distillation Backdoor Attacks: Backdoors Can Really Survive in Knowledge Distillation
In this paper, we, for the first time, propose a novel Anti-Distillation Backdoor Attack (ADBA), in which the backdoor embedded in the public teacher model can survive the knowledge distillation process and thus be transferred to secret distilled student models.
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information
For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary.
Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images
In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation.
Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties
By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction.
Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network
However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice.
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
Electrical peak demand forecasting- A review
To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature.
Intrusion Detection and Localization for Networked Embedded Control Systems
In this paper we present a physics-based Intrusion Detection System (IDS) aimed at increasing the security in control systems.
CARTL: Cooperative Adversarially-Robust Transfer Learning
In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain.
FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving.
How to distribute data across tasks for meta-learning?
We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks.
A bottom-up quantification of flexibility potential from the thermal energy storage in electric space heating
To this end, dwellings with heat pumps and direct electric heaters are modeled as thermal energy storage equivalents that can be included in a linear two-stage problem formulation.
Modularity and Mutual Information in Networks: Two Sides of the Same Coin
At a high level, we show the significance of community structure is equivalent to the amount of information contained in the network.
Community Detection
Social and Information Networks
DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques
Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors.
Recent Advances in Adversarial Training for Adversarial Robustness
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.
Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
Specifically, we formulate it as a 3D semantic segmentation problem to identify material types for all voxels based on which contraband materials can be detected.
Data Augmentation with norm-VAE for Unsupervised Domain Adaptation
As a result, our proposed methods (i. e. naive-SPL and norm-VAE-SPL) can achieve new state-of-the-art performance with the average accuracy of 93. 4% and 90. 4% on Office-Caltech and ImageCLEF-DA datasets, and comparable performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97. 2%, 87. 6% and 67. 9% respectively.
Touch Editing: A Flexible One-Time Interaction Approach for Translation
We propose a touch-based editing method for translation, which is more flexible than traditional keyboard-mouse-based translation postediting.
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.
Collaborative Fairness in Federated Learning
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.
Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders
In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source domain samples as semantic representations for zero-shot learning.
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibited item detection focus primarily on 2D X-ray imagery.
Learning Error-Driven Curriculum for Crowd Counting
Density regression has been widely employed in crowd counting.
Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint
We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC).
Information Theory Networking and Internet Architecture Signal Processing Information Theory
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model.
An Auto-Context Deformable Registration Network for Infant Brain MRI
Deformable image registration is fundamental to longitudinal and population analysis.
A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e. g., texts and images) or feature dimensions (e. g., features extracted with different methods).
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth
In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images with small slice spacing from low-resolution (LR) inputs of large slice spacing.
On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery
As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features.
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Breast cancer is one of the most common and deadliest cancers among women.
Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world
Japan and Italy are in serious situations with no short-term end to the outbreak to be expected.
Populations and Evolution Biological Physics Applications
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures.
Publicly Verifiable Databases With All Efficient Updating Operations
As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion.
Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions
This paper proposes a new scheme for privacy-preserving neural network prediction in the outsourced setting, i. e., the server cannot learn the query, (intermediate) results, and the model.
A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes
Threat Image Projection (TIP) is a technique used in X-ray security baggage screening systems that superimposes a threat object signature onto a benign X-ray baggage image in a plausible and realistic manner.
Robust Brain Magnetic Resonance Image Segmentation for Hydrocephalus Patients: Hard and Soft Attention
Brain magnetic resonance (MR) segmentation for hydrocephalus patients is considered as a challenging work.
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants.
Ranked #5 on
Crowd Counting
on ShanghaiTech B
(using extra training data)
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains.
Ranked #1 on
Domain Adaptation
on Office-Caltech
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection
Collaborative learning allows multiple clients to train a joint model without sharing their data with each other.
An End-to-End Network for Co-Saliency Detection in One Single Image
Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed.
The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composited X-ray Imagery
This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery.
NCLS: Neural Cross-Lingual Summarization
Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.
advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns
The results demonstrate that deep re-ID systems are vulnerable to our physical attacks.
Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors
Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis
To address this issue, we propose a simple but effective strategy, that is, we propose a dual-discriminator (dual-D) adversarial learning system, in which, a global-D is used to make an overall evaluation for the synthetic image, and a local-D is proposed to densely evaluate the local regions of the synthetic image.
Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization
Medical image segmentation is challenging especially in dealing with small dataset of 3D MR images.
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to.
DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT
Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.
An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.
Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition
Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain.
Ranked #3 on
Domain Adaptation
on Office-Caltech
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction.
Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details.
Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images
Thus, the two trajectories can act as a bridge to link the fixed and the moving images, and guide their registration.
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc.
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i. e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client.
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.
Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks
We present a method for reading digital data embedded in planar 3D printed surfaces.
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.
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network.
Block Matching Frame based Material Reconstruction for Spectral CT
Spectral computed tomography (CT) has a great potential in material identification and decomposition.
Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes
Cultural heritage is the asset of all the peoples of the world.
