SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 12261250 of 10419 papers

TitleStatusHype
BiasPruner: Debiased Continual Learning for Medical Image ClassificationCode1
Enrich the content of the image Using Context-Aware Copy Paste0
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image ClassificationCode1
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical0
MambaVision: A Hybrid Mamba-Transformer Vision BackboneCode7
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image ClassificationCode0
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
Trainable Highly-expressive Activation FunctionsCode1
Towards a text-based quantitative and explainable histopathology image analysisCode0
Exploring Camera Encoder Designs for Autonomous Driving Perception0
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification0
CTRL-F: Pairing Convolution with Transformer for Image Classification via Multi-Level Feature Cross-Attention and Representation Learning FusionCode0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images0
Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches0
Momentum Auxiliary Network for Supervised Local LearningCode1
Wavelet Convolutions for Large Receptive FieldsCode4
An accurate detection is not all you need to combat label noise in web-noisy datasetsCode0
FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance0
Evaluating the Fairness of Neural Collapse in Medical Image Classification0
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification0
Multi-Label Plant Species Classification with Self-Supervised Vision TransformersCode1
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Leveraging Topological Guidance for Improved Knowledge DistillationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified