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Found 44 papers, 23 papers with code
SimpleNLG-TI: Adapting SimpleNLG to Tibetan
Surface realisation is the last but not the least phase of Natural Language Generation, which aims to produce high-quality natural language text based on meaning representations.
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields.
Information Cascade Prediction under Public Emergencies: A Survey
With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies.
Robust Light-Weight Facial Affective Behavior Recognition with CLIP
Human affective behavior analysis aims to delve into human expressions and behaviors to deepen our understanding of human emotions.
Robust COVID-19 Detection in CT Images with CLIP
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data.
Preserving Fairness Generalization in Deepfake Detection
The existing method for addressing this problem is providing a fair loss function.
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation.
Detecting Multimedia Generated by Large AI Models: A Survey
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.
Quantum Probability Theoretic Asset Return Modeling: A Novel Schrödinger-Like Trading Equation and Multimodal Distribution
The complex phase of quantum probability, capturing transitions between long and short decisions while considering information interaction among traders, offers an inherent advantage over classical probability in characterizing the multimodal distribution of asset returns. Utilizing Fourier decomposition, we derive a Schr\"odinger-like trading equation, where each term explicitly corresponds to implications of market trading.
ZONE: Zero-Shot Instruction-Guided Local Editing
We then propose a Region-IoU scheme for precise image layer extraction from an off-the-shelf segment model.
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation
Recently, Segment Anything Model (SAM) has shown promising performance in some medical image segmentation tasks, but it performs poorly for liver tumor segmentation.
Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior
Recently, there is a trend to incorporate deep learning into the scanning process to further increase the scanning speed. Yet, most such attempts are performed for raster scanning while those for rotational scanning are relatively rare.
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models
The first stage pre-trains the student model on a large number of filtered multi-modal datasets.
PRIOR: Prototype Representation Joint Learning from Medical Images and Reports
In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports.
JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection
To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans.
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
The statistical heterogeneity (e. g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model.
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images.
The inverse Cox-Ingersoll-Ross process for parsimonious financial price modeling
We propose a formulation to construct new classes of financial price processes based on the insight that the key variable driving prices $P$ is the earning-over-price ratio $\gamma \simeq 1/P$, which we refer to as the earning yield and is analogous to the yield-to-maturity of an equivalent perpetual bond.
UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment
Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications.
YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation
With only one noisy skeleton annotation (respectively 0. 14\%, 0. 03\%, 1. 40\%, and 0. 65\% of the full annotation), YoloCurvSeg achieves more than 97\% of the fully-supervised performance on each dataset.
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution
We propose the first character-level white-box adversarial attack method against transformer models.
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph
First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner.
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network
Furthermore, with only 5% paired data, the proposed DS3-Net achieves competitive performance with state-of-theart image translation methods utilizing 100% paired data, delivering an average SSIM of 0. 8947 and an average PSNR of 23. 60.
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification
We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images.
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student.
JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection
At the coarse stage, we obtain the OD/OC coarse segmentation and the heatmap localization of fovea through a joint segmentation and detection module.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Pair-Level Supervised Contrastive Learning for Natural Language Inference
We adopt a cross attention module to learn the joint representations of the sentence pairs.
What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation
To alleviate the knowledge forgetting issue, we design two modules, Im and Gm, for each type of knowledge, which are combined via prompt tuning.
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.
COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading
In this paper, we propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading.
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.
BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.
A New Physically Triggered Cell Death via Transbarrier Contactless Cold Atmospheric Plasma Treatment of Cancer Cells
The physical-triggered growth inhibition is due to a new type of cell death, characterized by rapid leakage of bulk water from the cells, resulting in bubbles on the cell membrane, and cytoplasm shrinkage.
An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning
It combines the output probabilities of samples and their corresponding label numbers to get final prediction results.
An Approach for Process Model Extraction By Multi-Grained Text Classification
Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.
Remaining useful life estimation of engineered systems using vanilla LSTM neural networks
Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises.
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
We propose a novel image classification method based on learning hierarchical inter-class structures.
G-Bean: an ontology-graph based web tool for biomedical literature retrieval
To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: parallel document index creation, ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database.
