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 10251–10300 of 10420 papers

TitleStatusHype
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection—0
Leveraging CNNs and Ensemble Learning for Automated Disaster Image Classification—0
Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings—0
Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification—0
Leveraging counterfactual concepts for debugging and improving CNN model performance—0
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis—0
Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification—0
Leveraging feature communication in federated learning for remote sensing image classification—0
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks—0
Leveraging Internal Representations of Model for Magnetic Image Classification—0
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild—0
Text Descriptions are Compressive and Invariant Representations for Visual Learning—0
Leveraging Perceptual Scores for Dataset Pruning in Computer Vision Tasks—0
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data—0
Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Leveraging Systematic Knowledge of 2D Transformations—0
Leveraging Text-to-Image Generation for Handling Spurious Correlation—0
Break a Lag: Triple Exponential Moving Average for Enhanced Optimization—0
Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images—0
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks—0
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups—0
LiftPool: Bidirectional ConvNet Pooling—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
LightGBM robust optimization algorithm based on topological data analysis—0
Light Lies: Optical Adversarial Attack—0
Lightweight Adaptive Feature De-drifting for Compressed Image Classification—0
Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts—0
Linear Context Transform Block—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification—0
Linked Adapters: Linking Past and Future to Present for Effective Continual Learning—0
Linking ImageNet WordNet Synsets with Wikidata—0
Lipschitz-Bounded Equilibrium Networks—0
Lipschitz Bounded Equilibrium Networks—0
LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification—0
LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification—0
Interpreting and Improving Attention From the Perspective of Large Kernel Convolution—0
LLBoost: Last Layer Perturbation to Boost Pre-trained Neural Networks—0
LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification—0
LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection—0
LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors—0
LMM-Regularized CLIP Embeddings for Image Classification—0
LMSA: Low-relation Mutil-head Self-Attention Mechanism in Visual Transformer—0
Local Binary Pattern(LBP) Optimization for Feature Extraction—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