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Found 11 papers, 4 papers with code
EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the corresponding images, a phenomenon known as object hallucination.
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization.
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data.
DRIN: Dynamic Relation Interactive Network for Multimodal Entity Linking
Multimodal Entity Linking (MEL) is a task that aims to link ambiguous mentions within multimodal contexts to referential entities in a multimodal knowledge base.
Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level
The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism.
Deep-Learning-based Vasculature Extraction for Single-Scan Optical Coherence Tomography Angiography
In comparison to OCTA images obtained via the SV-OCTA (PSNR: 17. 809) and ED-OCTA (PSNR: 18. 049) using four-repeated OCT scans, OCTA images extracted by VET exhibit moderate quality (PSNR: 17. 515) and higher image contrast while reducing the required data acquisition time from ~8 s to ~2 s. Based on visual observations, the proposed VET outperforms SV and ED algorithms when using neck and face OCTA data in areas that are challenging to scan.
CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification
In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task.
EPCS: Endpoint-based Part-aware Curve Skeleton Extraction for Low-quality Point Clouds
In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed.
An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems
Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.
Machine Learning in Heterogeneous Porous Materials
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.
An End-to-End Attack on Text-based CAPTCHAs Based on Cycle-Consistent Generative Adversarial Network
Firstly, we train CAPTCHA synthesizers based on the cycle-GAN to generate some fake samples.
