SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–575 of 2042 papers

TitleStatusHype
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition—0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
A Novel Equation based Classifier for Detecting Human in Images—0
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments—0
A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations—0
Categorical Mixture Models on VGGNet activations—0
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
Dreaming with ARC—0
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation—0
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition—0
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks—0
Cascade Region Proposal and Global Context for Deep Object Detection—0
An optical biomimetic eyes with interested object imaging—0
A Non-Technical Survey on Deep Convolutional Neural Network Architectures—0
Capturing the objects of vision with neural networks—0
Adversarial Examples on Object Recognition: A Comprehensive Survey—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
An online passive-aggressive algorithm for difference-of-squares classification—0
Capacity limitations of visual search in deep convolutional neural networks—0
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified