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 751–775 of 2042 papers

TitleStatusHype
Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery—0
In Silico Modelling of Neurodegeneration Using Deep Convolutional Neural Networks—0
Out-of-distribution robustness: Limited image exposure of a four-year-old is enough to outperform ResNet-50—0
Bio-inspired learnable divisive normalization for ANNs—0
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object RecognitionCode0
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model—0
ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices—0
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—0
A Multi-viewpoint Outdoor Dataset for Human Action RecognitionCode0
MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual RepresentationsCode0
Empowering Local Communities Using Artificial Intelligence—0
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks—0
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects—0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs—0
Disentangling Properties of Contrastive Methods—0
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization NetworksCode0
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?Code0
Superquadric Object Representation for Optimization-based Semantic SLAM—0
Class incremental learning for video action classification—0
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
Object recognition for robotics from tactile time series data utilising different neural network architectures—0
Temporal RoI Align for Video Object Recognition—0
Capturing the objects of vision with neural networks—0
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition—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