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 7601–7625 of 10420 papers

TitleStatusHype
Information-theoretical label embeddings for large-scale image classification—0
Deep Kernel Learning via Random Fourier Features—0
BA^2M: A Batch Aware Attention Module for Image Classification—0
A Method for Restoring the Training Set Distribution in an Image Classifier—0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Information contraction in noisy binary neural networks and its implications—0
Joint Device-Edge Inference over Wireless Links with Pruning—0
ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification—0
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates—0
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding—0
InfoDisent: Explainability of Image Classification Models by Information Disentanglement—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification—0
A Melting Pot of Evolution and Learning—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
Prototypical Clustering Networks for Dermatological Disease Diagnosis—0
Inference with Hybrid Bio-hardware Neural Networks—0
Pro-tuning: Unified Prompt Tuning for Vision Tasks—0
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers—0
Deep Image Retrieval is not Robust to Label Noise—0
Provable Robustness for Streaming Models with a Sliding Window—0
Trade-offs between membership privacy & adversarially robust learning—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Inductive Transfer for Neural Architecture Optimization—0
Inducing Functions through Reinforcement Learning without Task Specification—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