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Found 12 papers, 2 papers with code
DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation
Compositional text-to-video generation, which requires synthesizing dynamic scenes with multiple interacting entities and precise spatial-temporal relationships, remains a critical challenge for diffusion-based models.
Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection: Balancing Security and Data Protection
Privacy-preserving network anomaly detection has become an essential area of research due to growing concerns over the protection of sensitive data.
Object Detection for Medical Image Analysis: Insights from the RT-DETR Model
Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges.
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
The research results show that deep learning, especially convolutional neural networks, have significant advantages in medical image classification tasks, especially in pneumonia X-ray image analysis, and can provide efficient and accurate automatic diagnosis support.
Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis
The model's performance is underscored by its high metrics in Precision, Accuracy, and F1-Score, indicating its potential for real-world application in glaucoma detection.
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study
This paper proposes a medical literature summary generation method based on the BERT model to address the challenges brought by the current explosion of medical information.
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection
This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis.
Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision
Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter.
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios
Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.
TransFA: Transformer-based Representation for Face Attribute Evaluation
The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning.
Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning
In the critic network, a supervised diagnosis model for disease predictions is involved to precisely estimate the state-value function.
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis
To address the challenge, we propose a non-RL Bipartite Scalable framework for Online Disease diAgnosis, called BSODA.
