Solving The Data Bottleneck For Physical AI

Data is the life-blood of physical AI. Collecting real-life data is expensive. Generative AI and diffusion to create ...
IEEE

Mask-DiFuser: A Masked Diffusion Model for Unified Unsupervised Image Fusion

Abstract: The absence of ground truth (GT) in most fusion tasks poses significant challenges for model optimization, evaluation, and generalization. Existing fusion methods achieving complementary ...
GitHub

SVG-T2I: Scaling up Text-to-Image Latent Diffusion Model

Important Note: This repository implements SVG-T2I, a text-to-image diffusion framework that performs visual generation directly in Visual Foundation Model (VFM) representation space, rather than ...
Frontiers

Parkinson's disease detection using spectrogram-based multi-model feature fusion networks

This study proposes a spectrogram-based deep learning framework for PD classification, leveraging time-frequency representations of speech signals to capture discriminative acoustic patterns. The ...
C&EN

Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...
Frontiers

Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification

Before feature extraction, we need to preprocess the original image, which mainly includes two steps: image positioning segmentation and bright spot filling. This phenomenon occurs because the eyeball ...
marktechpost

Meta AI Just Released DINOv3: A State-of-the-Art Computer Vision Model Trained with Self-Supervised Learning, Generating High-Resolution Image Features

DINOv3 represents a major leap in computer vision: its frozen universal backbone and SSL approach enable researchers and developers to tackle annotation-scarce tasks, deploy high-performance models ...
GitHub

This project is for the midterm assignment which is to deploy a deep learning image classification for flower dataset

I used google colab to train the model. First, the user needs to download and store the dataset from kaggle and so he should use the code in kaggle_lib.py. Then, The user will copy the colab_model.py ...