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

Showing 37013725 of 10420 papers

TitleStatusHype
On the Robustness of deep learning-based MRI Reconstruction to image transformations0
Soft Augmentation for Image ClassificationCode1
Framework Construction of an Adversarial Federated Transfer Learning Classifier0
Learning advisor networks for noisy image classificationCode0
Understanding the Role of Mixup in Knowledge Distillation: An Empirical StudyCode0
Detecting Shortcuts in Medical Images -- A Case Study in Chest X-raysCode0
Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach0
FIXED: Frustratingly Easy Domain Generalization with Mixup0
Temporal superimposed crossover module for effective continuous sign language0
MogaNet: Multi-order Gated Aggregation NetworkCode2
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification0
KGTN-ens: Few-Shot Image Classification with Knowledge Graph EnsemblesCode0
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression0
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search0
WaveNets: Wavelet Channel Attention NetworksCode0
Deep neural network based on F-neurons and its learningCode0
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks0
Evaluating a Synthetic Image Dataset Generated with Stable Diffusion0
Exploring Explainability Methods for Graph Neural Networks0
Untargeted Backdoor Attack against Object DetectionCode1
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid0
Chinese CLIP: Contrastive Vision-Language Pretraining in ChineseCode5
WITT: A Wireless Image Transmission Transformer for Semantic CommunicationsCode2
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency DomainCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified