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 376400 of 10419 papers

TitleStatusHype
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
MetaLA: Unified Optimal Linear Approximation to Softmax Attention MapCode1
Vision Eagle Attention: a new lens for advancing image classificationCode1
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image ClassificationCode1
Training objective drives the consistency of representational similarity across datasetsCode1
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language ModelsCode1
Interpretable Image Classification with Adaptive Prototype-based Vision TransformersCode1
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
Is Less More? Exploring Token Condensation as Training-free Adaptation for CLIPCode1
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
GlobalMamba: Global Image Serialization for Vision MambaCode1
Robust 3D Point Clouds Classification based on Declarative DefendersCode1
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing AttentionCode1
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space ModelCode1
Parameter Efficient Fine-tuning via Explained Variance AdaptationCode1
NegMerge: Consensual Weight Negation for Strong Machine UnlearningCode1
FACMIC: Federated Adaptative CLIP Model for Medical Image ClassificationCode1
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image ClassificationCode1
MONICA: Benchmarking on Long-tailed Medical Image ClassificationCode1
Vision-Language Models are Strong Noisy Label DetectorsCode1
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path AggregationCode1
Realistic Evaluation of Model Merging for Compositional GeneralizationCode1
Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoECode1
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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