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Found 30 papers, 14 papers with code
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
These spatial features then undergo intermediate temporal modeling facilitated by the Mamba block before progressing to the encoder section, which comprises vanilla upsampling Shift S-GCN blocks.
LATIS: Lambda Abstraction-based Thermal Image Super-resolution
Single image super-resolution (SISR) is an effective technique to improve the quality of low-resolution thermal images.
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images.
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance
The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions.
ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition
However, the combination of pose, visual information, and text attributes has not been explored yet, though text and pose attributes independently have been proven to be effective in numerous computer vision tasks.
FAST: Font-Agnostic Scene Text Editing
However, most of the existing STE methods show inferior editing performance because of (1) complex image backgrounds, (2) various font styles, and (3) varying word lengths within the text.
DySTreSS: Dynamically Scaled Temperature in Self-Supervised Contrastive Learning
We propose a cosine similarity-dependent temperature scaling function to effectively optimize the distribution of the samples in the feature space.
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation
Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc.
MMC: Multi-Modal Colorization of Images using Textual Descriptions
Handling various objects with different colors is a significant challenge for image colorization techniques.
Global Context-Aware Person Image Generation
The proposed strategy enables us to synthesize semantically coherent realistic persons that can blend into an existing scene without altering the global context.
TIPS: Text-Induced Pose Synthesis
In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person.
Scene Aware Person Image Generation through Global Contextual Conditioning
Finally, the target image is generated from the refined skeleton using another generative network conditioned on a given image of the target person.
SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification
Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data.
Multi-scale Attention Guided Pose Transfer
Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose.
SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification
This results in robust discriminative learning of the embedding space.
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning
After 200 epochs of pre-training with ResNet-18 as the backbone, the proposed model achieves an accuracy of 86. 2\%, 58. 18\%, 77. 49\%, and 30. 87\% on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet datasets, respectively, and surpasses the SOTA contrastive baseline by 1. 23\%, 3. 57\%, 2. 00\%, and 0. 33\%, respectively.
Attention W-Net: Improved Skip Connections for better Representations
We propose Attention W-Net, a new U-Net based architecture for retinal vessel segmentation to address these problems.
GradML: A Gradient-based Loss for Deep Metric Learning
Deep metric learning (ML) uses a carefully designed loss function to learn distance metrics for improving the discriminatory ability for tasks like clustering and retrieval.
LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning
Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc.
PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection
Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms.
Multipath Graph Convolutional Neural Networks
Recent research has focused on stacking multiple layers like in convolutional neural networks for the increased expressive power of graph convolution networks.
SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data
The downstream task in our paper is a class imbalanced multi-label classification.
Ranked #2 on
Multi-Label Classification
on MRNet
Position and Rotation Invariant Sign Language Recognition from 3D Kinect Data with Recurrent Neural Networks
Sign language is a gesture-based symbolic communication medium among speech and hearing impaired people.
A Data-driven Understanding of COVID-19 Dynamics Using Sequential Genetic Algorithm Based Probabilistic Cellular Automata
In this work, a probabilistic cellular automata based method has been employed to model the infection dynamics for a significant number of different countries.
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos
In this paper, we propose a self-supervised learning approach to learn transferable features from MR video clips by enforcing the model to learn anatomical features.
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction
Although state-of-the-art deep learning based methods have been able to obtain fast, high-quality reconstruction of CS-MR images, their main drawback is that they treat complex-valued MRI data as real-valued entities.
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks
Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the acquisition of MR images by breaking the Nyquist sampling limit.
STEFANN: Scene Text Editor using Font Adaptive Neural Network
In this paper, we propose a method to modify text in an image at character-level.
Effects of Degradations on Deep Neural Network Architectures
Deep convolutional neural networks (CNN) have massively influenced recent advances in large-scale image classification.
