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

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Showing 6251–6300 of 10420 papers

TitleStatusHype
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models—0
Memory Efficient Class-Incremental Learning for Image Classification—0
Memory-efficient Patch-based Inference for Tiny Deep Learning—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification—0
MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation—0
Robust Multi-instance Learning with Stable Instances—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA—0
Going Deeper Into Face Detection: A Survey—0
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks—0
Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification—0
SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification—0
Distribution-Aware Adaptive Multi-Bit Quantization—0
Meta Approach to Data Augmentation Optimization—0
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck—0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data—0
Meta-causal Learning for Single Domain Generalization—0
Goal-driven text descriptions for images—0
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training—0
Coresets for Robust Training of Neural Networks against Noisy Labels—0
A Capsule Network for Hierarchical Multi-Label Image Classification—0
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning—0
GmNet: Revisiting Gating Mechanisms From A Frequency View—0
Coreset Selection for Object Detection—0
GM-Net: Learning Features with More Efficiency—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
GM-MLIC: Graph Matching based Multi-Label Image Classification—0
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks—0
Meta Knowledge Distillation—0
Divergent Search for Few-Shot Image Classification—0
COPS: Controlled Pruning Before Training Starts—0
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—0
Meta-Learner with Linear Nulling—0
Diverse Feature Learning by Self-distillation and Reset—0
GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes—0
MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification—0
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification—0
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization—0
MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Asynchronous SGD without gradient delay for efficient distributed training—0
Global-to-Local Support Spectrums for Language Model Explainability—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