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 1776–1800 of 2042 papers

TitleStatusHype
Compressing Deep Convolutional Networks using Vector Quantization—0
Compression of Deep Neural Networks on the Fly—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
Connecting metrics for shape-texture knowledge in computer vision—0
Consistency of Silhouettes and Their Duals—0
Instance Scale Normalization for image understanding—0
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models—0
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
Content Placement in Networks of Similarity Caches—0
Context Augmentation for Convolutional Neural Networks—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
Context-driven Visual Object Recognition based on Knowledge Graphs—0
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—0
Contextual Recurrent Convolutional Model for Robust Visual Learning—0
Continual Hyperbolic Learning of Instances and Classes—0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
Continual Learning for Pose-Agnostic Object Recognition in 3D Point Clouds—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
Controlled-rearing studies of newborn chicks and deep neural networks—0
Controlled Tactile Exploration and Haptic Object Recognition—0
Convex Class Model on Symmetric Positive Definite Manifolds—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