Image Categorization

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (61 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
  11 Feb 2020 3 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
• Text-Guided Mixup Towards Long-Tailed Image Categorization
  5 Sep 2024 1 repository listed
  In many real-world applications, the frequency distribution of class labels for training data can exhibit a long-tailed distribution, which challenges traditional approaches of training deep neural networks that require…
• Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human vision
  7 Sep 2023 1 repository listed
  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
  5 Sep 2022 1 repository listed
  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
  17 Jun 2022 1 repository listed
  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
  3 Jun 2022 1 repository listed
  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
  7 May 2022 1 repository listed
  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…
• Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
  23 Nov 2021 1 repository listed
  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
  19 Aug 2021 1 repository listed Syntology ran 8 of 11 samples · 3 unverified
  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…
• diffGrad: An Optimization Method for Convolutional Neural Networks
  12 Sep 2019 1 repository listed
  In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.
• Learning what and where to attend
  22 May 2018 1 repository listed
  Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
• KS(conf ): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications
  11 Apr 2018 1 repository listed
  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.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.