Search Results for author:
Found 9 papers, 5 papers with code
RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers
This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts.
ReFuSeg: Regularized Multi-Modal Fusion for Precise Brain Tumour Segmentation
This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains.
UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts
Given the current state of the world, because of existing situations around the world, millions of people suffering from mental illnesses feel isolated and unable to receive help in person.
SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers
This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks.
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-Resolution
Because of the spectral range mismatch between the images, Guided Super-Resolution of thermal images utilizing visible range images is difficult.
Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems
This is especially the case for computer vision models.
Performance evaluation of deep segmentation models for Contrails detection
In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones.
UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy.
Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data
The magnitude of a model's performance is proportional to this shift in the distribution of the dataset.
