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 1026–1050 of 2042 papers

TitleStatusHype
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects—0
The purpose of qualia: What if human thinking is not (only) information processing?—0
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates—0
The Ripple Pond: Enabling Spiking Networks to See—0
The role of temporal cortex in the control of attention—0
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks—0
The same but different: impact of animal facility sanitary status on a transgenic mouse model of Alzheimer's disease—0
The Semantics of Image Annotation—0
The Toybox Dataset of Egocentric Visual Object Transformations—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.—0
Thinning Algorithm Using Hypergraph Based Morphological Operators—0
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition—0
Three Guidelines of Online Learning for Large-Scale Visual Recognition—0
Through-Wall Object Recognition and Pose Estimation—0
Tiled convolutional neural networks—0
Timely Object Recognition—0
TKD: Temporal Knowledge Distillation for Active Perception—0
To Boost or not to Boost: On the Limits of Boosted Neural Networks—0
Top-Down Regularization of Deep Belief Networks—0
Topologically-Guided Color Image Enhancement—0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments—0
Topology Maintained Structure Encoding—0
Toward a Geometric Theory of Manifold Untangling—0
Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis—0
Towards Bayesian Deep Learning: A Framework and Some Existing Methods—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