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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1426–1450 of 10419 papers

TitleStatusHype
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
Occam Gradient DescentCode0
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation—0
LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
I Bet You Did Not Mean That: Testing Semantic Importance via BettingCode0
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
Verifiably Robust Conformal PredictionCode0
Improved Generation of Adversarial Examples Against Safety-aligned LLMsCode1
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap—0
Confidence-aware multi-modality learning for eye disease screeningCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution—0
DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive ArchitectureCode1
Why are Visually-Grounded Language Models Bad at Image Classification?Code2
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average—0
Model-Agnostic Zeroth-Order Policy Optimization for Meta-Learning of Ergodic Linear Quadratic Regulators—0
On Understanding Attention-Based In-Context Learning for Categorical Data—0
Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image ClassificationCode0
AdaFisher: Adaptive Second Order Optimization via Fisher InformationCode2
Demystify Mamba in Vision: A Linear Attention PerspectiveCode3
Accelerating Transformers with Spectrum-Preserving Token MergingCode2
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
ModelLock: Locking Your Model With a Spell—0
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified