Search Results for author:
Found 22 papers, 17 papers with code
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model.
DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarization
Instead of using a simple vision transformer block to extract information from the image patches, the proposed architecture uses two transformer blocks for greater coverage of the extracted feature space on a global and local scale.
A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement
Instead of using a simple ViT and hard splitting of images for the document image enhancement task, we employed a progressive tokenization technique to capture this local information from an image to achieve more effective results.
MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug-Target Interactions Prediction
Drug-target interactions (DTIs) is essential for advancing pharmaceuticals.
Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping
Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer and CoAtNet.
Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction
In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic.
Effective Document Image Enhancement Using tokens-to-token Transformer Network
Instead of using a simple ViT and hard splitting of images for the document image enhancement task, we employed a progressive tokeniza-tion technique to capture this local information from an image for achieving more effective results.
Ranked #1 on
Binarization
on H-DIBCO 2012
Local Window Attention Transformer for Polarimetric SAR Image Classification
The PolSARFormer outperformed the Swin Transformer and FNet by the margin of 5. 86% and 17. 63%, in terms of average accuracy in the San Francisco data benchmark.
OCFormer: One-Class Transformer Network for Image Classification
We propose a novel deep learning framework based on Vision Transformers (ViT) for one-class classification.
Deep Hyperspectral Unmixing using Transformer Network
In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep unmixing model with transformers.
Multimodal Fusion Transformer for Remote Sensing Image Classification
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification
The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps.
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
A different classifier from the generator and the discriminator is used in the 3D-HyperGAMO model, which is trained using both original and generated samples to {determine} the classes of newly generated samples to which they actually belong.
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands.
Ranked #1 on
Hyperspectral Image Classification
on Kennedy Space Center
(Overall Accuracy metric)
diffGrad: An Optimization Method for Convolutional Neural Networks
In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i. e., diffGrad).
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification.
Ranked #1 on
Hyperspectral Image Classification
on Salinas Scene
LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks
The proposed LiSHT is non-parametric and tackles the dying gradient problem.
FWLBP: A Scale Invariant Descriptor for Texture Classification
In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary Pattern (FWLBP).
Local Jet Pattern: A Robust Descriptor for Texture Classification
In this approach, a jet space representation of a texture image is derived from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so called local jet vectors (LJV), which also satisfy the Scale Space properties.
Fractal image compression using upper bound on scaling parameter
This paper presents a novel approach to calculate the affine parameters of fractal encoding, in order to reduce its computational complexity.
