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

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Showing 6551–6575 of 10420 papers

TitleStatusHype
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Convolutional Architecture Exploration for Action Recognition and Image Classification—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Nested-TNT: Hierarchical Vision Transformers with Multi-Scale Feature Processing—0
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases—0
Dual Complementary Dynamic Convolution for Image Recognition—0
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints—0
Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations—0
A survey on Semi-, Self- and Unsupervised Learning for 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
Generative Data Mining with Longtail-Guided Diffusion—0
Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure—0
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification—0
MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification—0
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training—0
Aggregating Local Deep Features for Image Retrieval—0
Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks—0
Dual-stream Network for Visual Recognition—0
Active Learning for Vision-Language Models—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified