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Found 28 papers, 7 papers with code
Applications and Challenges of AI and Microscopy in Life Science Research: A Review
This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges.
packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space
We study the online learning problem characterized by the varying input feature space of streaming data.
Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs
Handling haphazard streaming data, such as data from edge devices, presents a challenging problem.
Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the Sámi Language
ULR languages are those for which the amount of available textual resources is very low, and the speaker count for them is also very low.
Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis
The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications.
Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols
Dense Video Captioning (DVC) aims at detecting and describing different events in a given video.
No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values.
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems
Nearest neighbor (NN) sampling provides more semantic variations than pre-defined transformations for self-supervised learning (SSL) based image recognition problems.
Latent Graph Attention for Enhanced Spatial Context
In this paper, we present Latent Graph Attention (LGA) a computationally inexpensive (linear to the number of nodes) and stable, modular framework for incorporating the global context in the existing architectures, especially empowering small-scale architectures to give performance closer to large size architectures, thus making the light-weight architectures more useful for edge devices with lower compute power and lower energy needs.
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
Many real-world applications based on online learning produce streaming data that is haphazard in nature, i. e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features.
MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval
We present a novel Master Assistant Buddy Network (MABNet) for image retrieval which incorporates both learning mechanisms.
Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
We further show that when no pretraining is done or when the pretrained transformer models are used with non-natural images (e. g. medical data), CNNs tend to generalize better than transformers at even very small coreset sizes.
Ranked #3 on
Image Classification
on Tiny ImageNet Classification
MiShape: 3D Shape Modelling of Mitochondria in Microscopy
Fluorescence microscopy is a quintessential tool for observing cells and understanding the underlying mechanisms of life-sustaining processes of all living organisms.
Patch Gradient Descent: Training Neural Networks on Very Large Images
Traditional CNN models are trained and tested on relatively low resolution images (<300 px), and cannot be directly operated on large-scale images due to compute and memory constraints.
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?
A comparison between SOTA trackers using CNNs, transformers as well as the combination of the two is presented to study their stability at various compression ratios.
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning
To address this issue, partial binarization techniques have been developed, but a systematic approach to mixing binary and full-precision parameters in a single network is still lacking.
UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Based on the outlined issues, we introduce a novel research problem of training CNN models for very large images, and present 'UltraMNIST dataset', a simple yet representative benchmark dataset for this task.
Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering
Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost.
Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs
Streaming classification methods assume the number of input features is fixed and always received.
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells
Performing artificial intelligence (AI) tasks such as segmentation, tracking, and analytics of small sub-cellular structures such as mitochondria in microscopy videos of living cells is a prime example.
Single image dehazing for a variety of haze scenarios using back projected pyramid network
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging.
Ranked #2 on
Image Dehazing
on Dense-Haze
Neural network based country wise risk prediction of COVID-19
Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic.
TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC
In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases.
Can We Automate Diagrammatic Reasoning?
Preliminary results indicate that the domain is highly related to computer vision and pattern recognition research with several challenging avenues.
Polygonal approximation of digital planar curve using novel significant measure
The technique starts with finest initial segmentation points of a curve.
Are object detection assessment criteria ready for maritime computer vision?
However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting.
Raw-to-Raw: Mapping between Image Sensor Color Responses
We show that this approach achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations.
Geometric primitive feature extraction - concepts, algorithms, and applications
Three specific problems related to this topic have been studied, viz., polygonal approximation of digital curves, tangent estimation of digital curves, and ellipse fitting anddetection from digital curves.
