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 8151–8200 of 10420 papers

TitleStatusHype
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural NetworksCode0
Outside the Box: Abstraction-Based Monitoring of Neural NetworksCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
Deep Learning based HEp-2 Image Classification: A Comprehensive Review—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Auto-Precision Scaling for Distributed Deep LearningCode0
Rethinking deep active learning: Using unlabeled data at model trainingCode0
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation ApproachCode0
Visualization approach to assess the robustness of neural networks for medical image classification—0
IC-Network: Efficient Structure for Convolutional Neural Networks—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
Feedback Control for Online Training of Neural Networks—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)—0
Learning Permutation Invariant Representations using Memory NetworksCode0
Crowd Counting via Segmentation Guided Attention Networks and Curriculum LossCode0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Fine-Grained Neural Architecture Search—0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Learning with Hierarchical Complement Objective—0
Coverage Testing of Deep Learning Models using Dataset Characterization—0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Selective sampling for accelerating training of deep neural networksCode0
Explanatory Masks for Neural Network Interpretability—0
TinyCNN: A Tiny Modular CNN Accelerator for Embedded FPGA—0
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image ClassificationCode0
Label-similarity Curriculum Learning—0
Simple iterative method for generating targeted universal adversarial perturbationsCode0
In-domain representation learning for remote sensingCode0
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer—0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique—0
Adversarial Transformations for Semi-Supervised Learning—0
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation—0
Image-Based Feature Representation for Insider Threat Classification—0
Cost-efficient segmentation of electron microscopy images using active learning—0
Pose Guided Attention for Multi-label Fashion Image Classification—0
Throughput Prediction of Asynchronous SGD in TensorFlow—0
A Computing Kernel for Network Binarization on PyTorchCode0
An empirical study of the relation between network architecture and complexity—0
Learning From Brains How to Regularize MachinesCode0
Meta Label Correction for Noisy Label LearningCode0
IrisNet: Deep Learning for Automatic and Real-time Tongue Contour Tracking in Ultrasound Video Data using Peripheral Vision—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
On the design of convolutional neural networks for automatic detection of Alzheimer's diseaseCode0
Improving Machine Reading Comprehension via Adversarial Training—0
On the Relationship between Self-Attention and Convolutional LayersCode0
Knowledge Distillation for Incremental Learning in Semantic Segmentation—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—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