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 6251–6275 of 10420 papers

TitleStatusHype
Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation—0
Memory Efficient Class-Incremental Learning for Image Classification—0
Adaptive Test-Time Augmentation for Low-Power CPU—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning—0
Learning Structured Inference Neural Networks with Label Relations—0
Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence—0
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking—0
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning—0
Learning Soft Labels via Meta Learning—0
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks—0
Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer's Disease—0
Design of Real-time Semantic Segmentation Decoder for Automated Driving—0
Analyzing the Dependency of ConvNets on Spatial Information—0
Meta Approach to Data Augmentation Optimization—0
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network—0
Learning scale-variant and scale-invariant features for deep image classification—0
Meta-causal Learning for Single Domain Generalization—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Learning rich optical embeddings for privacy-preserving lensless image classification—0
Learning Representations of Graph Data -- A Survey—0
Learning Representation in Colour Conversion—0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation—0
Analyzing Images for Music Recommendation—0
Adaptive Temperature Scaling with Conformal Prediction—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