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 4601–4625 of 10420 papers

TitleStatusHype
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain ShiftCode0
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network—0
Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN)—0
4Weed Dataset: Annotated Imagery Weeds Dataset—0
VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer DatasetCode1
Nested Collaborative Learning for Long-Tailed Visual RecognitionCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
CHEX: CHannel EXploration for CNN Model CompressionCode1
Edge Detection and Deep Learning Based SETI Signal Classification Method—0
Parameter-efficient Model Adaptation for Vision TransformersCode1
Treatment Learning Causal Transformer for Noisy Image Classification—0
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness—0
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
Neurosymbolic hybrid approach to driver collision warning—0
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language ModelCode1
WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse CodingCode0
Knowledge Mining with Scene Text for Fine-Grained RecognitionCode1
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks—0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization PerspectiveCode1
On the Viability of Monocular Depth Pre-training for Semantic SegmentationCode0
Uncertainty-aware Contrastive Distillation for Incremental Semantic SegmentationCode1
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness—0
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network CalibrationCode1
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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