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DeepLearning

Reading Notes for “CellPose”

Abstract for this paper: Cell segmentation is important for various downstream tasks. This paper introduces a generalist segmentation method called Cellpose that can segment cells from various ranges of image types without any fine-tuning. They also collect a large dataset containing over 70000 segmented objects to train the model. Finally, they built soft..

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DeepLearning

3D semantic segmentation for Electron Microscopy Dataset with nn-Unet

Dataset The dataset from the EPFL's CVLab represents a section of the CA1 hippocampus region of the brain, with a volume of 1065x2048x1536 and a voxel resolution of approximately 5x5x5nm. It's available as multipage TIF files and includes annotations for mitochondria in two sub-volumes, aimed at aiding research in accurately segmenting mitochondria and syn..

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DeepLearningActionRecognization

Reading Notes for "TimeSformer"

Abstract for this paper: This paper focuses on introducing Transformer architecture to video recognition to replace 3D CNN due to various benefits. They first used a complete formula to derive the method of calculating attention and building a model in video situations. Then, to reduce the computational cost, they try several attention schemes and finally ..

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DeepLearningActionRecognization

Animal Kingdom Dataset

Abstract Information: Animal action recognition is really important for various fields, such as animal behavior science and the protection and management of wildlife. Meanwhile, various action recognition methods based on Deep Learning request bigger datasets to train. Animal Kingdom which is a large dataset containing many species in different scenes and ..

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DeepLearningActionRecognization

Reading Notes for "TSN"

Abstract for this paper: Video-based tasks such as Action Recognition rely on long-range temporal information. Some methods use LSTM or other RNNs after feature extraction to utilize this information, but it will add more compute costs. Furthermore, the dominant end-to-end CNN model in video-based tasks is still lacking. This paper proposes TSN, which is a..

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DeepLearningActionRecognization

Reading Notes for "C3D"

Abstract for this paper: In the past, most Action Recognition methods are based on manual features. Although some models use deep learning, they just use 2D ConvNets. This paper explores various 3D convolution kernels having different depths. After that, it proposes a C3D model that can perform well after being trained in a large dataset. The authors use C..

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DeepLearningActionRecognization

Reading Notes for "Two-Stream CNN"

Abstract for this paper: Action Recognition is an important field in various vision tasks. Before this paper, most works were based on something other than Deep Learning, and although some papers tried using CNN, they couldn’t perform comparably. This paper proposes a Two-Stream ConvNet which introduces optical flow in architecture. This model is trained a..

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DeepLearningActionRecognization

The datasets for Action Recognization

How to build a dataset Define an action list, by combining labels from the previous action list and adding more categories depending on the use case. Obtain videos from various sources, such as movies or streaming media. Provide temporal annotations manually. Clean up the dataset by de-duplication and filtering Datasets list Datasets list HMDB51: It ..

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DeepLearningDiffusion

Reading Notes for "InverseSR"

Abstract for this paper: High-resolution magnetic resonance imaging (MRI) scans are important to provide precise information about imaged tissues. Thus, we need to find the method for image super-resolution. Traditional methods based on end-to-end deep learning have to be retrained when the distribution of input shifts. This paper proposed a new method to ..

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DeepLearningGenerativeModels

Introduction to Generative Models

Generative Models are part of unsupervised learning models that can learned from the datasets without any labels. Unlike other unsupervised models to manipulate, denoise, interpolate between, or compress examples, generative models focus on generating plausible new samples having similar properties to the dataset. Taxonomy of unsupervised learning models..

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