Search Results for author:
Found 48 papers, 17 papers with code
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models
To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives.
Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC
With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements.
MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine
The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts.
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations.
Understanding Overfitting in Adversarial Training via Kernel Regression
Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks.
HD-GCN:A Hybrid Diffusion Graph Convolutional Network
HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix.
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost.
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.
AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer
In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED.
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation
First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.
Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening
Particularly, we first present a style-oriented replay module to enable structure-realistic and memory-efficient reproduction of past data, and then incorporate the replayed past data to jointly optimize the model with current data to alleviate catastrophic forgetting.
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images.
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures
To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation
The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire.
FlowX: Towards Explainable Graph Neural Networks via Message Flows
We investigate the explainability of graph neural networks (GNNs) as a step towards elucidating their working mechanisms.
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving.
HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy
Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.
Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image
For diagnosis of shoulder illness, it is essential to look at the morphology deviation of scapula and humerus from the medical images that are acquired from Magnetic Resonance (MR) imaging.
SoK: On the Semantic AI Security in Autonomous Driving
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar
Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.
Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach
In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels.
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network
In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities.
Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN(SRG-Net)
Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.
Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing
Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems.
Self-paced Resistance Learning against Overfitting on Noisy Labels
They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.
SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis
Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds.
On Explainability of Graph Neural Networks via Subgraph Explorations
To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs.
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.
Unsupervised Segmentation for Terracotta Warrior Point Cloud (SRG-Net)
Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e. g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity.
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation
Medical image annotations are prohibitively time-consuming and expensive to obtain.
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.
Dynamic asymptotic dimension for actions of virtually cyclic groups
We show that the dynamic asymptotic dimension of a minimal free action of an infinite virtually cyclic group on a compact Hausdorff space is always one.
Dynamical Systems Group Theory Operator Algebras Primary: 37C45, Secondary: 37B05, 20F69
Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
However, effective and efficient delineation of all the knee articular cartilages in large-sized and high-resolution 3D MR knee data is still an open challenge.
Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms
We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud.
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.
Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks
This study presents a starting point toward a powerful tool for automatic classification of gait disorders and can be used as a basis for future applications of Deep Learning in clinical gait analysis.
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data
For the initial shoulder preoperative diagnosis, it is essential to obtain a three-dimensional (3D) bone mask from medical images, e. g., magnetic resonance (MR).
Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution
The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications
This paper presents a downscaling attack that targets the data scaling process in deep learning applications.
Temporal Subspace Clustering for Human Motion Segmentation
Subspace clustering is an effective technique for segmenting data drawn from multiple subspaces.
Recognition of convolutional neural network based on CUDA Technology
For the problem whether Graphic Processing Unit(GPU), the stream processor with high performance of floating-point computing is applicable to neural networks, this paper proposes the parallel recognition algorithm of Convolutional Neural Networks(CNNs). It adopts Compute Unified Device Architecture(CUDA)technology, definite the parallel data structures, and describes the mapping mechanism for computing tasks on CUDA.
