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 6551–6600 of 10420 papers

TitleStatusHype
Convolutional Clustering for Unsupervised Learning—0
MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition—0
Generic Image Classification Approaches Excel on Face Recognition—0
A Survey on Visual Transformer—0
Generative NeuroEvolution for Deep Learning—0
Dual Complementary Dynamic Convolution for Image Recognition—0
Convolutional autoencoder-based multimodal one-class classification—0
A Dot Product Attention Free Transformer—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations—0
Convolutional Architecture Exploration for Action Recognition and Image Classification—0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
Multi-domain learning CNN model for microscopy image classification—0
Multi-Domain Learning with Modulation Adapters—0
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure—0
Neural Architecture Design and Robustness: A Dataset—0
MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification—0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework—0
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases—0
Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks—0
Dual-stream Network for Visual Recognition—0
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
A survey on Semi-, Self- and Unsupervised Learning for Image Classification—0
Multi-Instance Learning by Utilizing Structural Relationship among Instances—0
Multi-Instance Multi-Scale CNN for Medical Image Classification—0
Multi-Instance Visual-Semantic Embedding—0
Multi-Label Adversarial Perturbations—0
Dual-Tree Wavelet Packet CNNs for Image Classification—0
Network Architecture Search for Domain Adaptation—0
Generative Data Mining with Longtail-Guided Diffusion—0
Network Augmentation for Tiny Deep Learning—0
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains—0
Aggregating Local Deep Features for Image Retrieval—0
Active Learning for Vision-Language Models—0
Multi-Label Image Classification with Regional Latent Semantic Dependencies—0
Multi-Label Image Classification with Contrastive Learning—0
Generative Adversarial Zero-shot Learning via Knowledge Graphs—0
Generative Adversarial Networks for Bitcoin Data Augmentation—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Dynamically Stable Poincaré Embeddings for Neural Manifolds—0
Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection—0
Multilayer deep feature extraction for visual texture recognition—0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption—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