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 21262150 of 10420 papers

TitleStatusHype
Automating Continual LearningCode1
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models0
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class ForgettingCode1
BCN: Batch Channel Normalization for Image ClassificationCode1
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion ModelsCode2
CLIP-QDA: An Explainable Concept Bottleneck Model0
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor0
Negotiated Representations to Prevent Forgetting in Machine Learning ApplicationsCode0
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation0
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans0
Rethinking Image Editing Detection in the Era of Generative AI Revolution0
Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels0
LayerCollapse: Adaptive compression of neural networks0
Meta Co-Training: Two Views are Better than OneCode1
Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification0
Do text-free diffusion models learn discriminative visual representations?Code1
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context0
Automatic Recognition of Learning Resource Category in a Digital LibraryCode0
PHG-Net: Persistent Homology Guided Medical Image ClassificationCode1
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection0
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model0
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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