Search Results for author:
Found 68 papers, 32 papers with code
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration
To bridge the data gap, a domain adaptation strategy is proposed to build the feature projection between the source and target domains by dynamically aligning their codebook embeddings, and a correlation alignment-based test-time adaptation mechanism is designed to fine-tune the alignment discrepancies by tightening the degradation embeddings to the corresponding cluster center in the source domain.
Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
While Transformer-based methods effectively capture global dependencies, they often overlook short-term local variations in time series.
SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models
Subjective Answer Grading (SAG) plays a crucial role in education, standardized testing, and automated assessment systems, particularly for evaluating short-form responses in Short Answer Scoring (SAS).
Seed1.5-VL Technical Report
We present Seed1. 5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning.
Ranked #1 on
Video Question Answering
on TVBench
Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision.
Emergence of Painting Ability via Recognition-Driven Evolution
The palette branch learns a limited colour palette, while the stroke branch parameterises each stroke using B\'ezier curves to render an image, subsequently evaluated by a high-level recognition module.
Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions.
Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes
Recent advances in human motion prediction (HMP) have shifted focus from isolated motion data to integrating human-scene correlations.
Human motion prediction
Human-Object Interaction Detection
+1
Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection
To tackle these issues, we propose D2UE, a Diversified Dual-space Uncertainty Estimation framework for medical anomaly detection.
FIF-UNet: An Efficient UNet Using Feature Interaction and Fusion for Medical Image Segmentation
Nowadays, pre-trained encoders are widely used in medical image segmentation because of their ability to capture complex feature representations.
Aligning Medical Images with General Knowledge from Large Language Models
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising generalization ability.
Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning
In this work, we propose a geometry-aware semi-supervised framework for fine-grained building function recognition, utilizing geometric relationships among multi-source data to enhance pseudo-label accuracy in semi-supervised learning, broadening its applicability to various building function categorization systems.
SkyDiffusion: Ground-to-Aerial Image Synthesis with Diffusion Models and BEV Paradigm
In this paper, we introduce SkyDiffusion, a novel cross-view generation method for synthesizing aerial images from street view images, utilizing a diffusion model and the Bird's-Eye View (BEV) paradigm.
ProSpec RL: Plan Ahead, then Execute
Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition.
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath).
DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering
Retrieval-Augmented Generation (RAG) has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA).
Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection
(3) Theoretical derivation and experimental evidence of reduced expert capacity bounds under dynamic token distribution evolution.
Tunable Superconducting Magnetic Levitation with Self-Stability
For the first time, we experimentally demonstrate a self-stable type II superconducting maglev system which is able to: counteract long term levitation force decay, adjust levitation force and equilibrium position, and establish levitation under zero field cooling condition.
Prompt-Guided Adaptive Model Transformation for Whole Slide Image Classification
To address this issue, we propose PAMT, a novel Prompt-guided Adaptive Model Transformation framework that enhances MIL classification performance by seamlessly adapting pre-trained models to the specific characteristics of histopathology data.
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification.
LocMoE: A Low-Overhead MoE for Large Language Model Training
However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity.
BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei.
ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning
Radio speech echo is a specific phenomenon in the air traffic control (ATC) domain, which degrades speech quality and further impacts automatic speech recognition (ASR) accuracy.
Ranked #31 on
Speech Enhancement
on VoiceBank + DEMAND
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adversarial Medical Image with Hierarchical Feature Hiding
Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs.
WinNet: Make Only One Convolutional Layer Effective for Time Series Forecasting
Deep learning models have recently achieved significant performance improvements in time series forecasting.
Ranked #15 on
Time Series Forecasting
on ETTh1 (336) Multivariate
DED: Diagnostic Evidence Distillation for acne severity grading on face images
In this study, we propose an acne diagnosis method, Diagnostic Evidence Distillation (DED), that suitably adapts the characteristics of acne diagnosis and can be applied to diagnose under different acne criteria.
Ranked #1 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Continuous 3D Myocardial Motion Tracking via Echocardiography
Myocardial motion tracking stands as an essential clinical tool in the prevention and detection of cardiovascular diseases (CVDs), the foremost cause of death globally.
Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs
Rather, the key factor in increasing the number of transplants, decreasing waiting times and improving group fairness is the judicious assignment of a negative weight (penalty) to the small number of non-directed donors in the kidney exchange program.
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
A method for measuring the synthesis complexity is proposed to automatically determine the synthesis order in our sequential GAN.
PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
To address these challenges, we propose diagnosis-driven prompts for medical report generation (PromptMRG), a novel framework that aims to improve the diagnostic accuracy of MRG with the guidance of diagnosis-aware prompts.
Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
The booming air transportation industry inevitably burdens air traffic controllers' workload, causing unexpected human factor-related incidents.
A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code Representation
Additionally, the Gray code representation and the differential prediction paradigm are designed to cope with the high-bit misclassifications of the BE representation, which significantly reduces the outliers in the predictions.
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Medical image segmentation is a fundamental task in the community of medical image analysis.
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020.
Disorder-invariant Implicit Neural Representation
However, the expressive power of INR is limited by the spectral bias in the network training.
Few Shot Medical Image Segmentation with Cross Attention Transformer
Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features.
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg).
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning
Multiple instance learning (MIL) has emerged as a popular method for classifying histopathology whole slide images (WSIs).
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Deep learning has significantly advanced medical imaging analysis (MIA), achieving state-of-the-art performance across diverse clinical tasks.
Learning a Room with the Occ-SDF Hybrid: Signed Distance Function Mingled with Occupancy Aids Scene Representation
We found that the color rendering loss results in optimization bias against low-intensity areas, causing gradient vanishing and leaving these areas unoptimized.
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
Despite incorporating the target features during training, the teacher model tends to overlook them under the inherent domain shift and task discrepancy.
Parallel Computing Based Solution for Reliability-Constrained Distribution Network Planning
The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained with specified reliability requirements.
Efficient Implicit Neural Reconstruction Using LiDAR
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage.
Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge
Acne Vulgaris seriously affects people’s daily life.
Ranked #2 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility
Although DRL has achieved important advancements in this field, the existing works pay little attention to the explainability and safety issues related to DRL controllers, particularly the safety under adversarial attacks.
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions
Over the past few years, the rapid development of deep learning technologies for computer vision has significantly improved the performance of medical image segmentation (MedISeg).
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression
In this study, we propose a novel multi-task framework, named Seg4Reg+, which jointly optimizes the segmentation and regression networks.
Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction
By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation.
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation
Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis.
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae.
KIEGLFN: A unified acne grading framework on face images
Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm.
Deep Learning for Computational Cytology: A Survey
Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening.
An Acne Grading Framework on Face Images via Skin Attention and SFNet
In this paper, the global estimation of acne severity grading is studied by Convolutional Neural Networks (CNNs) and a unified acne grading framework that can diagnose referring to different grading criteria is proposed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories
Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network
We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.
Speech recognition for air traffic control via feature learning and end-to-end training
In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches
Most importantly, a multi-modal speaker role identification network (MMSRINet) is designed to achieve the SRI task by considering both the speech and textual modality features.
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction
To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network.
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems
Finally, by integrating the SRL with ASR, an end-to-end multilingual ASR framework is formulated in a supervised manner, which is able to translate the raw wave into text in one model, i. e., wave-to-text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving speech recognition models with small samples for air traffic control systems
By virtue of the common terminology used in the ATC domain, the transfer learning task can be regarded as a sub-domain adaption task, in which the transferred model is optimized using a joint corpus consisting of baseline samples and new transcribed samples from the target dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.
Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes
In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes.
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment
To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
STELA: A Real-Time Scene Text Detector with Learned Anchor
To achieve high coverage of target boxes, a normal strategy of conventional one-stage anchor-based detectors is to utilize multiple priors at each spatial position, especially in scene text detection tasks.
Matrix embedding method in match for session-based recommendation
But this method repudiate the variousness of the interest of user in a session.
Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization
Experimental results show that our method can effectively synthesize a large variety of mpMRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship.
Synthesizing Multi-Parameter Magnetic Resonance Imaging (Mp-Mri) Data
Detecting Multi-Oriented Text with Corner-based Region Proposals
Previous approaches for scene text detection usually rely on manually defined sliding windows.
Ranked #1 on
Scene Text Detection
on COCO-Text
