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Found 14 papers, 12 papers with code
RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment.
Learning to recognize occluded and small objects with partial inputs
Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small.
CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets
FBGT involves the removal of real images that exhibit similarities to images of other classes before utilizing them as the training dataset for a GAN.
Knowledge Distillation approach towards Melanoma Detection
As well as with fewer learnable parameters, 0. 26 million (M) compared to 42. 5M using knowledge distillation with the goal to detect melanoma from dermoscopic images.
Masked Supervised Learning for Semantic Segmentation
Self-attention is of vital importance in semantic segmentation as it enables modeling of long-range context, which translates into improved performance.
Ranked #97 on
Semantic Segmentation
on NYU Depth v2
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout
Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout.
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification
However, recent SSL methods rely on unlabeled image data at a scale of billions to work well.
Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation
The U-Net architecture, built upon the fully convolutional network, has proven to be effective in biomedical image segmentation.
Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis
We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models.
ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans
We explore the idea of whether pretraining a model on realistic videos could improve performance rather than training the model from scratch, intended for tuberculosis type classification from chest CT scans.
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data.
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
A common approach to medical image analysis on volumetric data uses deep 2D convolutional neural networks (CNNs).
Melanoma Detection using Adversarial Training and Deep Transfer Learning
In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation.
Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks
The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture.
