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 401–425 of 2042 papers

TitleStatusHype
AI-based Density Recognition—0
Active Perception using Light Curtains for Autonomous Driving—0
Contextual Recurrent Convolutional Model for Robust Visual Learning—0
Artistic Object Recognition by Unsupervised Style Adaptation—0
Continual Hyperbolic Learning of Instances and Classes—0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models—0
DCI: Discriminative and Contrast Invertible Descriptor—0
Continual Learning for Pose-Agnostic Object Recognition in 3D Point Clouds—0
Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection—0
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling—0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Contrastive Object Detection Using Knowledge Graph Embeddings—0
Contrastive Reasoning in Neural Networks—0
Collaboration Analysis Using Deep Learning—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
Controlled Tactile Exploration and Haptic Object Recognition—0
Convex Class Model on Symmetric Positive Definite Manifolds—0
Convolutional Models for Joint Object Categorization and Pose Estimation—0
Convolutional Networks with Dense Connectivity—0
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future—0
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation—0
DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects—0
CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs—0
Convolutional Prototype Learning for Zero-Shot Recognition—0
Applications of Probabilistic Programming (Master's thesis, 2015)—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