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 1251–1275 of 2042 papers

TitleStatusHype
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features—0
State Classification with CNN—0
State-of-the-art Models for Object Detection in Various Fields of Application—0
State-Regularized Recurrent Neural Networks—0
State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions—0
Statistical Mechanics of Neural Processing of Object Manifolds—0
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons—0
Story-oriented Image Selection and Placement—0
Semantic and structural image segmentation for prosthetic vision—0
Structure-From-Motion and RGBD Depth Fusion—0
Submodular Object Recognition—0
Subsidiary Prototype Alignment for Universal Domain Adaptation—0
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
SUGAR: Pre-training 3D Visual Representations for Robotics—0
Superquadric Object Representation for Optimization-based Semantic SLAM—0
Surface Defects Detection of Transparent Plastic Bottles Based on Improved Yolov5—0
Surface Registration Using Genetic Algorithm in Reduced Search Space—0
Survey on Self-supervised Representation Learning Using Image Transformations—0
Suspicious Object Recognition Method in Video Stream Based on Visual Attention—0
SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet—0
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN—0
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation—0
Synthesising Dynamic Textures using Convolutional Neural Networks—0
Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter—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