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Found 16 papers, 1 papers with code
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification.
Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery
By enhancing the accuracy and efficiency of tree inventory, our model empowers urban management to mitigate the adverse effects of deforestation and climate change.
Efficient quantum image representation and compression circuit using zero-discarded state preparation approach
The encoding of images for representation and compression in quantum systems is different from classical ones.
Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis
The GPC model outperforms the other models with a specificity of 95. 78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.
A Network Theory Investigation into the Altered Resting State Functional Connectivity in Attention-Deficit Hyperactivity Disorder
In the last two decades, functional magnetic resonance imaging (fMRI) has emerged as one of the most effective technologies in clinical research of the human brain.
Adversarial Domain Adaptation for Action Recognition Around the Clock
This paper presents a domain adaptation-based action recognition approach that uses adversarial learning in cross-domain settings to learn cross-domain action recognition.
Efficient Diffusion Models for Vision: A Survey
In this review, we present the most recent advances in diffusion models for vision, specifically focusing on the important design aspects that affect the computational efficiency of DMs.
Vision Transformers for Action Recognition: A Survey
Finally, it provides a discussion on the challenges, outlook, and future avenues for this research direction.
Advance quantum image representation and compression using DCTEFRQI approach
Hilbert space or Euclidean space has infinite dimension to locate and process the image data faster.
Efficient dynamic point cloud coding using Slice-Wise Segmentation
In the proposed method, the entire point cloud has been cross-sectioned into variable-sized slices based on the number of self-occluded points so that data loss can be minimized in the patch generation process and projection.
Dynamic Point Cloud Compression with Cross-Sectional Approach
However, to broadcast successfully, the dynamic point clouds require higher compression due to their huge volume of data compared to the traditional video.
MAVIDH Score: A COVID-19 Severity Scoring using Chest X-Ray Pathology Features
As the primary contribution, this method correlates well to patient severity in different stages of disease progression with competitive results compared to other existing, more complex methods.
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework
However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data.
Potential Features of ICU Admission in X-ray Images of COVID-19 Patients
X-ray images may present non-trivial features with predictive information of patients that develop severe symptoms of COVID-19.
Real-time Plant Health Assessment Via Implementing Cloud-based Scalable Transfer Learning On AWS DeepLens
The process of testing an image for disease diagnosis and classification using AWS DeepLens on average took 0. 349s, providing disease information to the user in less than a second.
Computer Vision For COVID-19 Control: A Survey
The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population.
