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 801–825 of 2042 papers

TitleStatusHype
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition—0
AI-based Density Recognition—0
Active Perception using Light Curtains for Autonomous Driving—0
ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection—0
Forecasting Hands and Objects in Future Frames—0
Global Deconvolutional Networks for Semantic Segmentation—0
Formula-Supervised Visual-Geometric Pre-training—0
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition—0
Foveated Downsampling Techniques—0
Continual Hyperbolic Learning of Instances and Classes—0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models—0
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal—0
Context-Aware Zero-Shot Learning for Object Recognition—0
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured—0
From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes—0
From Virtual to Real: A Framework for Verbal Interaction with Robots—0
From Visual Attributes to Adjectives through Decompositional Distributional Semantics—0
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
Discriminatively Trained Sparse Code Gradients for Contour Detection—0
Brain-Like Object Recognition Neural Networks are more robustness to common corruptions—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