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 6501–6525 of 10420 papers

TitleStatusHype
The simpler the better: vanilla sgd revisited—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks—0
Reducing Implicit Bias in Latent Domain Learning—0
NOSE Augment: Fast and Effective Data Augmentation Without Searching—0
Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition—0
Adaptive Dataset Sampling by Deep Policy Gradient—0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Auto-view contrastive learning for few-shot image recognition—0
Robust early-learning: Hindering the memorization of noisy labels—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
Unsupervised Domain Adaptation via Minimized Joint Error—0
Towards Practical Second Order Optimization for Deep Learning—0
Graph Structural Aggregation for Explainable Learning—0
Feedforward Legendre Memory Unit—0
TwinDNN: A Tale of Two Deep Neural Networks—0
TaskSet: A Dataset of Optimization TasksCode0
Recall Loss for Imbalanced Image Classification and Semantic SegmentationCode1
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models—0
Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting—0
LLBoost: Last Layer Perturbation to Boost Pre-trained Neural Networks—0
Lipschitz-Bounded Equilibrium Networks—0
DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization—0
Optimal allocation of data across training tasks in meta-learning—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—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
10RevCol-HTop 1 Accuracy90—Unverified