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 101150 of 10419 papers

TitleStatusHype
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality InversionCode2
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual TasksCode2
Practical Continual Forgetting for Pre-trained Vision ModelsCode2
Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal UnderstandingCode2
TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response ScenariosCode2
MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image ClassificationCode2
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation LearningCode2
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge DistillationCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
TinyViM: Frequency Decoupling for Tiny Hybrid Vision MambaCode2
Task Singular Vectors: Reducing Task Interference in Model MergingCode2
EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space DualityCode2
BiomedCoOp: Learning to Prompt for Biomedical Vision-Language ModelsCode2
ScaleKD: Strong Vision Transformers Could Be Excellent TeachersCode2
Frontiers in Intelligent ColonoscopyCode2
Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State FusionCode2
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet UpcyclingCode2
One missing piece in Vision and Language: A Survey on Comics UnderstandingCode2
A Survey on Mixup Augmentations and BeyondCode2
PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease SegmentationCode2
The AdEMAMix Optimizer: Better, Faster, OlderCode2
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image ClassificationCode2
HAIR: Hypernetworks-based All-in-One Image RestorationCode2
SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-trainingCode2
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile ApplicationsCode2
VSSD: Vision Mamba with Non-Causal State Space DualityCode2
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?Code2
GroupMamba: Efficient Group-Based Visual State Space ModelCode2
DataDream: Few-shot Guided Dataset GenerationCode2
AWT: Transferring Vision-Language Models via Augmentation, Weighting, and TransportationCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent CollaborationCode2
Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIPCode2
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation LearningCode2
WATT: Weight Average Test-Time Adaptation of CLIPCode2
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%Code2
Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image ClassificationCode2
Parameter-Inverted Image Pyramid NetworksCode2
GrootVL: Tree Topology is All You Need in State Space ModelCode2
Why are Visually-Grounded Language Models Bad at Image Classification?Code2
AdaFisher: Adaptive Second Order Optimization via Fisher InformationCode2
Accelerating Transformers with Spectrum-Preserving Token MergingCode2
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern GeneratorsCode2
EMR-Merging: Tuning-Free High-Performance Model MergingCode2
Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image ClassificationCode2
SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch NormalizationCode2
Many-Shot In-Context Learning in Multimodal Foundation ModelsCode2
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNsCode2
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified