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 5826–5850 of 10420 papers

TitleStatusHype
Boosting Convolutional Features for Robust Object Proposals—0
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
Discovering Parametric Activation Functions—0
Discovering Influential Neuron Path in Vision Transformers—0
An Embedding-Dynamic Approach to Self-supervised Learning—0
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models—0
Discovering Discriminative Cell Attributes for HEp-2 Specimen Image Classification—0
Generating Hard Examples for Pixel-wise Classification—0
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks—0
Bombus Species Image Classification—0
DISCO: Distributed Inference with Sparse Communications—0
Block-wise Scrambled Image Recognition Using Adaptation Network—0
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency—0
Block-term Tensor Neural Networks—0
An Efficient Technique for Image Captioning using Deep Neural Network—0
Addressing out-of-distribution label noise in webly-labelled data—0
Addressing Imbalance for Class Incremental Learning in Medical Image Classification—0
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Dirichlet-based Histogram Feature Transform for Image Classification—0
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks—0
Blockchain Enabled Trustless API Marketplace—0
Blink: Fast and Generic Collectives for Distributed ML—0
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models—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