Search Results for author:
Found 12 papers, 8 papers with code
Zero-shot Disfluency Detection for Indian Languages
We present a detailed pipeline to synthetically generate disfluent text and create evaluation datasets for four Indian languages: Bengali, Hindi, Malayalam, and Marathi.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Towards Granularity-adjusted Pixel-level Semantic Annotation
Specifically, we propose an approach to enable the Segment Anything Model (SAM) with semantic recognition capability to generate pixel-level annotations for images without any manual supervision.
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task.
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches
In this paper, we push the boundary further for FSCIL by addressing two key questions that bottleneck its ubiquitous application (i) can the model learn from diverse modalities other than just photo (as humans do), and (ii) what if photos are not readily accessible (due to ethical and privacy constraints).
MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis.
Ranked #1 on
Semantic Segmentation
on PH2
Segmentation of Brain MRI using an Altruistic Harris Hawks' Optimization algorithm
In this paper, we perform the multi-level thresholding using an evolutionary metaheuristic.
Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification
Cervical cancer is the fourth most common category of cancer, affecting more than 500, 000 women annually, owing to the slow detection procedure.
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical Cytology
The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5‑fold cross‑validation scheme.
Ranked #3 on
Image Classification
on SIPaKMeD
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-Scans
COVID‑19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people.
Ranked #1 on
Image Classification
on SARS-COV-2
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection
A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the Grey Wolf Optimizer, thus improving the classification performance.
Ranked #1 on
Image Classification
on SIPaKMeD
Single Image Super-Resolution using Residual Channel Attention Network
In this paper, we propose a deep learning-based approach for the problem, wherein we use a fully convolutional attention network coupled with residual in the residual block (RIR), Residual Channel Attention Block (RCAB), and long and short skip connections.
Comparative study of maturation profiles of neural cells in different species with the help of Computer Vision and Deep Learning
Our results show different migration patterns between species suggesting different maturation profiles as well as different maturation rates of neuroblast cells in different species.
