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 7701–7725 of 10420 papers

TitleStatusHype
A Free-Energy Principle for Representation Learning—0
Collective Learning—0
Random Laplace Feature Maps for Semigroup Kernels on Histograms—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation—0
Rank4Class: A Ranking Formulation for Multiclass Classification—0
Quantifying error contributions of computational steps, algorithms and hyperparameter choices in image classification pipelines—0
Quantifying Task Complexity Through Generalized Information Measures—0
Quantifying task-relevant representational similarity using decision variable correlation—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices—0
Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50—0
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation—0
Exploring Structural Nonlinearity in Binary Polariton-Based Neuromorphic Architectures—0
Quantization of Deep Neural Networks for Accumulator-constrained Processors—0
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task—0
Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning—0
Quantized Feature Distillation for Network Quantization—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework—0
CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts—0
A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers—0
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified