Search Results for author:
Found 18 papers, 2 papers with code
CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning
However, a common problem among the RRT-based algorithms is that the initial path generated is not optimal and the convergence is too slow to be used in real-world applications.
ViTBIS: Vision Transformer for Biomedical Image Segmentation
Similarly, transformer blocks and multi scale architecture is used in decoder before being linearly projected to produce the output segmentation map.
AA-PINN: ATTENTION AUGMENTED PHYSICS INFORMED NEURAL NETWORKS
Physics Informed Neural Networks has been quite successful in modelling the complex nature of fluid flow.
AASeg: Attention Aware Network for Real Time Semantic Segmentation
In this paper, we present a new network named Attention Aware Network (AASeg) for real time semantic image segmentation.
AA3DNet: Attention Augmented Real Time 3D Object Detection
In this work, we address the problem of 3D object detection from point cloud data in real time.
DMSANet: Dual Multi Scale Attention Network
Attention mechanism of late has been quite popular in the computer vision community.
Generate Novel Molecules With Target Properties Using Conditional Generative Models
In this paper, we present a novel neural network for generating small molecules similar to the ones in the training set.
Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion
Depth estimation from monocular images is a challenging problem in computer vision.
HRVGAN: High Resolution Video Generation using Spatio-Temporal GAN
In this paper, we present a novel network for high resolution video generation.
Generate High Resolution Images With Generative Variational Autoencoder
In this work, we present a novel neural network to generate high resolution images.
Stochastic Bayesian Neural Networks
Our work builds on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound.
Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation
Deep learning motivated by convolutional neural networks has been highly successful in a range of medical imaging problems like image classification, image segmentation, image synthesis etc.
Bayesian Multi Scale Neural Network for Crowd Counting
However dense crowd counting remains an open problem because of severe occlusion and perspective view in which people can be present at various sizes.
Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars
In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation.
Ranked #68 on
Semantic Segmentation
on Cityscapes test
Bayesian Neural Network via Stochastic Gradient Descent
This work extends the work done by others by deriving the variational inference models.
On Using Transfer Learning For Plant Disease Detection
Our model achieves the best of results using ResNet50 with accuracy of 0. 982, precision of 0. 94, recall of 0. 94 and F1 score of 0. 94.
Convolutional Neural Networks for Classifying Melanoma Images
In this work, we address the problem of skin cancer classification using convolutional neural networks.
RUHSNet: 3D Object Detection Using Lidar Data in Real Time
In this work, we address the problem of 3D object detection from point cloud data in real time.
