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 701–725 of 2042 papers

TitleStatusHype
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method—0
Convolutional Models for Joint Object Categorization and Pose Estimation—0
Convex Class Model on Symmetric Positive Definite Manifolds—0
A shallow residual neural network to predict the visual cortex response—0
Controlled Tactile Exploration and Haptic Object Recognition—0
Controlled-rearing studies of newborn chicks and deep neural networks—0
A semantics-driven methodology for high-quality image annotation—0
AI-Powered GUI Attack and Its Defensive Methods—0
Adaptation Across Extreme Variations using Unlabeled Domain Bridges—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
Contrastive Reasoning in Neural Networks—0
A Self-supervised GAN for Unsupervised Few-shot Object Recognition—0
Contrastive Object Detection Using Knowledge Graph Embeddings—0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos—0
AI-Powered Augmented Reality for Satellite Assembly, Integration and Test—0
Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection—0
Continual Learning for Pose-Agnostic Object Recognition in 3D Point Clouds—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling—0
AI-Powered Assistive Technologies for Visual Impairment—0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models—0
Continual Hyperbolic Learning of Instances and Classes—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Contextual Recurrent Convolutional Model for Robust Visual Learning—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