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 9876–9900 of 10420 papers

TitleStatusHype
Inducing Functions through Reinforcement Learning without Task Specification—0
Inductive Transfer for Neural Architecture Optimization—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers—0
Inference with Hybrid Bio-hardware Neural Networks—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
InfoDisent: Explainability of Image Classification Models by Information Disentanglement—0
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates—0
Information contraction in noisy binary neural networks and its implications—0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Information-theoretical label embeddings for large-scale image classification—0
Informative Class Activation Maps—0
Informative Robust Causal Representation for Generalizable Deep Learning—0
Informed Non-convex Robust Principal Component Analysis with Features—0
In-Hindsight Quantization Range Estimation for Quantized Training—0
Initialization Noise in Image Gradients and Saliency Maps—0
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data—0
Initializing Perturbations in Multiple Directions for Fast Adversarial Training—0
In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Inplace knowledge distillation with teacher assistant for improved training of flexible deep neural networks—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks—0
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images—0
Inspect Transfer Learning Architecture with Dilated Convolution—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