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 50765100 of 10420 papers

TitleStatusHype
Building extraction with vision transformer0
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable PrototypesCode1
On the Effectiveness of Neural Ensembles for Image Classification with Small Datasets0
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images0
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images0
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification0
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual RecognitionCode1
Reinforcement Explanation Learning0
KNAS: Green Neural Architecture SearchCode1
ExPLoit: Extracting Private Labels in Split Learning0
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
FedDropoutAvg: Generalizable federated learning for histopathology image classification0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network0
ML-Decoder: Scalable and Versatile Classification HeadCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Global Interaction Modelling in Vision Transformer via Super Tokens0
Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?0
Transferability Estimation using Bhattacharyya Class Separability0
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates0
Sharpness-aware Quantization for Deep Neural NetworksCode1
PeCo: Perceptual Codebook for BERT Pre-training of Vision TransformersCode1
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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