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Found 29 papers, 5 papers with code
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval
Unsupervised image retrieval aims to learn the important visual characteristics without any given level to retrieve the similar images for a given query image.
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation
Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications.
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network
Motivated from the success of transformers in language and vision applications, we propose a SRTransGAN for image super-resolution using transformer based GAN.
PTSR: Patch Translator for Image Super-Resolution
The results of the proposed model is improved on an average for $4\times$ super-resolution by 21. 66% in PNSR score and 11. 59% in SSIM score, as compared to the best competitive models.
Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey
This paper presents a comprehensive survey on the developments and advancements in GANs utilizing the Transformer networks for computer vision applications.
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs
In this paper, we propose a novel AdaNorm based SGD optimizers by correcting the norm of gradient in each iteration based on the adaptive training history of gradient norm.
An Object Aware Hybrid U-Net for Breast Tumour Annotation
For polygon like annotation or segmentation, we have used Active Contours whose vertices or snake points move towards the boundary of the object of interest to find the region of minimum energy.
Bag of Visual Words (BoVW) with Deep Features -- Patch Classification Model for Limited Dataset of Breast Tumours
BoVW is used as a feature selector to select most discriminative features among the CNN features.
Ensembling Handcrafted Features with Deep Features: An Analytical Study for Classification of Routine Colon Cancer Histopathological Nuclei Images
The use of Deep Learning (DL) based methods in medical histopathology images have been one of the most sought after solutions to classify, segment, and detect diseased biopsy samples.
Cell nuclei classification in histopathological images using hybrid OLConvNet
To further strengthen the viability of our architectural approach, we tested our proposed methodology with state of the art deep learning architectures AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 as backbone networks.
Centre Symmetric Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval
Distinctive information captured by the kernel with limited number of pixel achieves satisfactory recognition and retrieval accuracies on facial images taken under constrained environment (controlled variations in light, pose, expressions, and background).
Cascaded Asymmetric Local Pattern: A Novel Descriptor for Unconstrained Facial Image Recognition and Retrieval
In this paper a novel hand crafted cascaded asymmetric local pattern (CALP) is proposed for retrieval and recognition facial image.
Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval
Accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures.
R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval
RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths.
Local Directional Gradient Pattern: A Local Descriptor for Face Recognition
The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between the higher order derivatives of the reference pixel in four distinct directions.
Local Quadruple Pattern: A Novel Descriptor for Facial Image Recognition and Retrieval
The major problem in increasing the local neighbourhood is that, it also increases the feature length of the descriptor.
Scene Graph Generation with Geometric Context
We use this post-processing algorithm to add and refine the geometric relationships between object pairs to a prior model.
Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark
The most popular and common non-linearity layers are activation functions (AFs), such as Logistic Sigmoid, Tanh, ReLU, ELU, Swish and Mish.
AdaInject: Injection Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks
Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.
Vision Transformer Hashing for Image Retrieval
We utilize the pre-trained ViT on ImageNet as the backbone network and add the hashing head.
Lossy Medical Image Compression using Residual Learning-based Dual Autoencoder Model
In this work, we propose a two-stage autoencoder based compressor-decompressor framework for compressing malaria RBC cell image patches.
Signature Verification using Geometrical Features and Artificial Neural Network Classifier
Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method.
A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images
As a part of this work, we also present a new and unique database for obtaining the region of interest in thermal images based on an existing thermal visual paired database, containing the Region of Interest on 5 different classes of data.
An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification
However, due to patch-based analysis, most of the current methods fail to exploit the underlying spatial relationship among the patches.
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images
We propose a learning-based compression scheme that envelopes a standard codec between pre and post-processing deep CNNs.
Face Recognition Using $Sf_{3}CNN$ With Higher Feature Discrimination
A video, instead of an image, as an input can be more useful to solve the challenges of face recognition in real world conditions.
Face Recognition using 3D CNNs
In this work, we used video as input to the 3D CNN architectures for capturing both spatial and time domain information from the video for face recognition in real world environment.
A Simple Mutual Information based Registration Method for Thermal-Optical Image Pairs applied on a Novel Dataset
While thermal optical registered datasets are becoming widely available, most of these works are based on image pairs which are pre-registered.
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).
