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 7701–7750 of 10420 papers

TitleStatusHype
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
Collective Learning—0
Connectivity Learning in Multi-Branch Networks—0
ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification—0
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation—0
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers—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
Real-time Detection of Practical Universal Adversarial Perturbations—0
FLOAT: FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS—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
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification—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
A Sparse Coding Interpretation of Neural Networks and Theoretical Implications—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
Reading Is Believing: Revisiting Language Bottleneck Models for Image Classification—0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
Quantum Circuit C^*-algebra Net—0
Quantum-classical convolutional neural networks in radiological image classification—0
R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut—0
Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer—0
Quantum Enhanced Filter: QFilter—0
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting—0
Quantum Entanglement in Deep Learning Architectures—0
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding—0
Flatten: Video Action Recognition is an Image Classification task—0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
Connecting metrics for shape-texture knowledge in computer vision—0
A Free-Energy Principle for Representation Learning—0
Real-time convolutional networks for sonar image classification in low-power embedded systems—0
Quantum Phase Recognition using Quantum Tensor Networks—0
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning—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
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection—0
A Smart System for Selection of Optimal Product Images in E-Commerce—0
Confusable Learning for Large-class Few-Shot Classification—0
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How—0
A Framework using Contrastive Learning for Classification with Noisy Labels—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