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 39263950 of 10420 papers

TitleStatusHype
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language ModelsCode2
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks0
Visual Recognition with Deep Nearest CentroidsCode1
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks0
Medical Image Segmentation using LeViT-UNet++: A Case Study on GI Tract Data0
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition0
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
On the interplay of adversarial robustness and architecture components: patches, convolution and attention0
A novel illumination condition varied image dataset-Food Vision Dataset (FVD) for fair and reliable consumer acceptability predictions from food0
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation0
PaLI: A Jointly-Scaled Multilingual Language-Image Model0
ConvNeXt Based Neural Network for Audio Anti-SpoofingCode0
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends0
Learning Deep Optimal Embeddings with Sinkhorn Divergences0
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep LearningCode1
Revisiting Neural Scaling Laws in Language and Vision0
Unsupervised representation learning with recognition-parametrised probabilistic modelsCode0
Class-Level Logit PerturbationCode0
Virtual Underwater Datasets for Autonomous Inspections0
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation0
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision TransformersCode1
Moving from 2D to 3D: volumetric medical image classification for rectal cancer stagingCode0
A Capsule Network for Hierarchical Multi-Label Image Classification0
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model GeneralizationCode1
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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