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Found 25 papers, 6 papers with code
Eye-gaze Guided Multi-modal Alignment Framework for Radiology
Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal pre-training.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing
Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink).
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
However, most existing FL works are not conducted on datasets collected from authentic IoT devices that capture unique modalities and inherent challenges of IoT data.
Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps
In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
FedMultimodal: A Benchmark For Multimodal Federated Learning
In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities.
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
Through comprehensive ablation analysis, we discover that the downstream model generated by synthetic data plays a crucial role in controlling the direction of gradient diversity during FL training, which enhances convergence speed and contributes to the notable accuracy boost observed with GPT-FL.
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.
ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
However, we show empirically that this method can lead to a substantial drop in training accuracy as well as a slower convergence rate.
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.
Rectify ViT Shortcut Learning by Visual Saliency
In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data.
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.
Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution
The convolution on mesh considers the spatial organization of functional gradients and folding patterns on a cortical sheet and the newly designed channel attention block enhances the interpretability of the contribution of different functional gradients to cortical folding prediction.
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs).
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances.
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications.
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning
In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters.
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices.
Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles
Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints.
