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 7651–7700 of 10420 papers

TitleStatusHype
Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples—0
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels—0
Pseudo Labels for Single Positive Multi-Label Learning—0
Florida Wildlife Camera Trap Dataset—0
Detached Error Feedback for Distributed SGD with Random Sparsification—0
Pseudo Rehearsal using non photo-realistic images—0
Consensus Clustering With Unsupervised Representation Learning—0
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift—0
Exploration of Noise Strategies in Semi-supervised Named Entity Classification—0
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks—0
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks—0
Connectivity Learning in Multi-Branch Networks—0
ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
Explore the Power of Dropout on Few-shot Learning—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Purification Of Contaminated Convolutional Neural Networks Via Robust Recovery: An Approach with Theoretical Guarantee in One-Hidden-Layer Case—0
Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models—0
Pushing Joint Image Denoising and Classification to the Edge—0
Pushing the envelope in deep visual recognition for mobile platforms—0
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information—0
Exploring Category-correlated Feature for Few-shot Image Classification—0
FLOAT: FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS—0
Radio Signal Classification by Adversarially Robust Quantum Machine Learning—0
Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency—0
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification—0
A Sparse Coding Interpretation of Neural Networks and Theoretical Implications—0
Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks—0
RadTex: Learning Efficient Radiograph Representations from Text Reports—0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
Flatten: Video Action Recognition is an Image Classification task—0
Pyramidal RoR for Image Classification—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
Connecting metrics for shape-texture knowledge in computer vision—0
A Free-Energy Principle for Representation Learning—0
RAID: Randomized Adversarial-Input Detection for Neural Networks—0
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks—0
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models—0
Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning—0
CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts—0
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning—0
Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm—0
A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers—0
Query-Efficient Decision-based Black-Box Patch Attack—0
A Smart System for Selection of Optimal Product Images in E-Commerce—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