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 8301–8350 of 10420 papers

TitleStatusHype
Distilling Effective Supervision from Severe Label NoiseCode0
Global Feature Guided Local Pooling—0
Gated Linear NetworksCode0
Meta-learning algorithms for Few-Shot Computer VisionCode0
XNOR-Net++: Improved Binary Neural Networks—0
Test-Time Training with Self-Supervision for Generalization under Distribution ShiftsCode0
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image ClassificationCode0
Fusion of Convolutional Neural Network and Statistical Features for Texture classification—0
Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image ClassificationCode0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
Interpreting Undesirable Pixels for Image Classification on Black-Box Models—0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Urban Sound Tagging using Convolutional Neural NetworksCode0
Two-stage Image Classification Supervised by a Single Teacher Single Student ModelCode0
Adaptive Binary-Ternary Quantization—0
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network—0
Balanced Binary Neural Networks with Gated ResidualCode0
Diving into Optimization of Topology in Neural Networks—0
A Kolmogorov Complexity Approach to Generalization in Deep Learning—0
Non-imaging single-pixel sensing with optimized binary modulation—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
Model-Agnostic Feature Selection with Additional Mutual Information—0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Adaptive Data Augmentation with Deep Parallel Generative Models—0
Monte Carlo Deep Neural Network Arithmetic—0
Adapting to Label Shift with Bias-Corrected Calibration—0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS—0
Beyond image classification: zooplankton identification with deep vector space embeddings—0
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search—0
AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training—0
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Distance-based Composable Representations with Neural Networks—0
Gated Channel Transformation for Visual RecognitionCode0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Laconic Image Classification: Human vs. Machine Performance—0
Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks—0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS—0
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep NetworksCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Invariance vs Robustness of Neural Networks—0
When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet—0
Smart Ternary Quantization—0
Training Data Distribution Search with Ensemble Active Learning—0
Unknown-Aware Deep Neural Network—0
Scalable Deep Neural Networks via Low-Rank Matrix Factorization—0
Test-Time Training for Out-of-Distribution Generalization—0
Siamese Attention Networks—0
SoftAdam: Unifying SGD and Adam for better stochastic gradient descent—0
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters—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