Search Results for author:
Found 35 papers, 15 papers with code
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation
To tackle these issues, we propose SimZSS, a Simple framework for open-vocabulary Zero-Shot Segmentation.
Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation
This oversight leads to skewed BN statistics and undermines the reliability of the model under non-i. i. d.
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping
Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images.
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
Inspired by a modern self-supervised vision transformer model trained using partial image inputs to reconstruct missing image regions -- we propose AMAE, a two-stage algorithm for adaptation of the pre-trained masked autoencoder (MAE).
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories.
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other.
Ranked #14 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning
Most self-supervised methods for representation learning leverage a cross-view consistency objective i. e., they maximize the representation similarity of a given image's augmented views.
TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain.
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime
Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality.
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
These pseudo labels are then used to train a node classification head for WSI segmentation.
ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
We further introduce a novel mixing data-augmentation, namely ScoreMix, by leveraging the image's semantic distribution to guide the data mixing and produce coherent sample-label pairs.
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations from a single labeled atlas to the unlabeled data.
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets.
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images
Addressing both issues, we propose a simple yet universal approach to improve the perceptual quality of the HR prediction from a pre-trained SR network on a given LR input by further fine-tuning the SR network on a subset of images from the training dataset with similar patterns of activation as the initial HR prediction, with respect to the filters of a feature extractor.
Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation
We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.
Self-Supervised Anomaly Detection
Semi-supervised Anomaly Detection
+1
Quantifying Explainers of Graph Neural Networks in Computational Pathology
However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario.
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues.
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer.
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs.
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
Revisiting Few-Shot Learning for Facial Expression Recognition
Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest impact on performance.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion
In this work, we demonstrate the benefit of using geometric information from synthetic images, coupled with scene depth information, to recover the scale in depth and ego-motion estimation from monocular videos.
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart.
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem.
Exploring Factors for Improving Low Resolution Face Recognition
By leveraging this information, we have utilized deep face models trained on MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.
Learn to synthesize and synthesize to learn
To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest.
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity.
Progressive Generative Adversarial Networks for Medical Image Super resolution
Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function.
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
We introduce a new scene graph generation method called image-level attentional context modeling (ILAC).
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity.
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution
We propose an image super resolution(ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of $16$.
