Image Categorization
11 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Image Categorization
Yeast colony morphologies
Latest papers
Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human vision
Subsequently, parallel CNN model is employed that uses combined 2D features for classifying images across COCO, Imagenet and SUN.
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition.
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
zeroonegame/cdnet-for-ous-fgic
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Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases.
Learning an Adaptation Function to Assess Image Visual Similarities
VieVie31/visual-similarity
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As a consequence, such features are powerful to compare semantically related images but not really efficient to compare images visually similar but semantically unrelated.
Ultrafast Image Categorization in Biology and Neural Models
To further the comparison between biological and artificial neural networks, we re-trained the standard VGG 16 CNN on two independent tasks that are ecologically relevant to humans: detecting the presence of an animal or an artifact.
Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
qzhb/tdcmn
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Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Unlike most existing methods that learn visual attention based on conventional likelihood, we propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process.
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
frgfm/Holocron
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The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
diffGrad: An Optimization Method for Convolutional Neural Networks
In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i. e., diffGrad).
Learning what and where to attend
Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
