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Found 13 papers, 5 papers with code
Unsupervised Video Summarization via Reinforcement Learning and a Trained Evaluator
In the proposed scheme, reinforcement learning, coupled with a unique reward generation pipeline, is employed to train the summarizer model.
Optimizing Split Points for Error-Resilient SplitFed Learning
Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machine learning.
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score
We show that the reconstruction loss of the model for a video with masked frames correlates with the representativeness of the remaining frames in the video.
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score.
We show that the reconstruction loss of the model for a video with masked frames correlates with the representativeness of the remaining frames in the video.
Ranked #1 on
Unsupervised Video Summarization
on TvSum
SplitFed resilience to packet loss: Where to split, that is the question
Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning (SplitFed or SFL).
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations
SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain.
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion
Time Series Classification (TSC) is an important and challenging task for many visual computing applications.
Ranked #1 on
Time Series Classification
on UEA
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation.
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation
Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis.
Ranked #1 on
Semantic Segmentation
on 38-Cloud
Preimplantation Blastomere Boundary Identification in HMC Microscopic Images of Early Stage Human Embryos
We present a novel method for identification of the boundary of embryonic cells (blastomeres) in Hoffman Modulation Contrast (HMC) microscopic images that are taken between day one to day three.
Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery
Cloud detection in satellite images is an important first-step in many remote sensing applications.
Ranked #2 on
Semantic Segmentation
on 38-Cloud
Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images.
CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images
Automatic detection of shadow regions in an image is a difficult task due to the lack of prior information about the illumination source and the dynamic of the scene objects.
