8 papers with code • 0 benchmarks • 1 datasets
These leaderboards are used to track progress in Image Categorization
Most implemented papers
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications
Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for days or even years as components of real-world commercial applications.
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).
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.
Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
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.
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.
Learning an Adaptation Function to Assess Image Visual Similarities
As a consequence, such features are powerful to compare semantically related images but not really efficient to compare images visually similar but semantically unrelated.
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases.