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 11761200 of 10420 papers

TitleStatusHype
DynaMixer: A Vision MLP Architecture with Dynamic MixingCode1
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and BeyondCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and BeyondCode1
EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature FusionCode1
Asymmetric Loss For Multi-Label ClassificationCode1
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge GraphsCode1
BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold NetworksCode1
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal RepresentationsCode1
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt TuningCode1
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention MechanismsCode1
Dual-Branch Subpixel-Guided Network for Hyperspectral Image ClassificationCode1
Fcaformer: Forward Cross Attention in Hybrid Vision TransformerCode1
Cached Transformers: Improving Transformers with Differentiable Memory CacheCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
Calibration of Neural Networks using SplinesCode1
Entropy-based Logic Explanations of Neural NetworksCode1
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural NetworkCode1
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional NetworkCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic CalibrationCode1
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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