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 901925 of 10419 papers

TitleStatusHype
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Fixing Localization Errors to Improve Image ClassificationCode1
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network InferenceCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path AggregationCode1
A Partially Reversible U-Net for Memory-Efficient Volumetric Image SegmentationCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
Florence: A New Foundation Model for Computer VisionCode1
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture SearchCode1
Focal and Global Knowledge Distillation for DetectorsCode1
Attention-Based Second-Order Pooling Network for Hyperspectral Image ClassificationCode1
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image ClassificationCode1
Adversarial Example Detection for DNN Models: A Review and Experimental ComparisonCode1
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image RecognitionCode1
Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image ClassificationCode1
Foundation Model Assisted Weakly Supervised Semantic SegmentationCode1
Fourier Image TransformerCode1
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image ClassificationCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic SparsityCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
A Comprehensive Survey on Graph Neural NetworksCode1
From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic SegmentationCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
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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