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 6351–6400 of 10420 papers

TitleStatusHype
Learning Multiple Non-linear Sub-spaces Using K-RBMs—0
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm—0
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization—0
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
A Classifier-guided Approach for Top-down Salient Object Detection—0
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning—0
Learning More Discriminative Local Descriptors for Few-shot Learning—0
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding—0
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification—0
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis—0
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection—0
Learning Loss for Test-Time Augmentation—0
Learning Long Sequences in Spiking Neural Networks—0
Learning Longer-term Dependencies in RNNs with Auxiliary Losses—0
Mixer: DNN Watermarking using Image Mixup—0
Depth Estimation with Simplified Transformer—0
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation—0
Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspective—0
Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification—0
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks—0
Dependency Decomposition and a Reject Option for Explainable Models—0
Learning Invariant Riemannian Geometric Representations Using Deep Nets—0
Learning Invariant Representations across Domains and Tasks—0
Density estimation in representation space to predict model uncertainty—0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework—0
Estimating the Generalization in Deep Neural Networks via Sparsity—0
Mixture of Experts in Image Classification: What's the Sweet Spot?—0
Learning Invariances in Neural Networks from Training Data—0
Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Learning Interpretable Logic Rules from Deep Vision Models—0
Learning Interclass Relations for Image Classification—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
Learning image quality assessment by reinforcing task amenable data selection—0
Learning Image Conditioned Label Space for Multilabel Classification—0
Learning Identity Mappings with Residual Gates—0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
Morphological Network: How Far Can We Go with Morphological Neurons?—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
MLP-based architecture with variable length input for automatic speech recognition—0
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input—0
A Classification Leveraged Object Detector—0
Visually Consistent Hierarchical Image Classification—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
Better (pseudo-)labels for semi-supervised instance segmentation—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