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 1601–1650 of 2042 papers

TitleStatusHype
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes—0
Deep Pyramidal Residual Networks with Separated Stochastic Depth—0
Deep Learning with Energy-efficient Binary Gradient Cameras—0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition—0
Learning Transferrable Representations for Unsupervised Domain Adaptation—0
Improved Deep Metric Learning with Multi-class N-pair Loss Objective—0
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems—0
Deep Learning for the Classification of Lung Nodules—0
Object Recognition with and without ObjectsCode0
Multi-Scale Saliency Detection using Dictionary Learning—0
Finding Mirror Symmetry via Registration—0
The Freiburg Groceries DatasetCode0
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation—0
Learning Detailed Face Reconstruction from a Single Image—0
Gradients of Counterfactuals—0
Crowdsourcing in Computer Vision—0
STDP-based spiking deep convolutional neural networks for object recognitionCode0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial ModelingCode0
Template Matching Advances and Applications in Image Analysis—0
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition—0
Are Accuracy and Robustness Correlated?—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Tangled Splines—0
Zero Shot Hashing—0
Egocentric Height Estimation—0
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
Recognizing Open-Vocabulary Relations between Objects in Images—0
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification—0
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition—0
Transfer Learning for Material Classification using Convolutional Networks—0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers—0
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity—0
Investigating Fluidity for Human-Robot Interaction with Real-time, Real-world Grounding Strategies—0
Ambient Sound Provides Supervision for Visual LearningCode0
Towards Bayesian Deep Learning: A Framework and Some Existing Methods—0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics—0
Adapting Deep Network Features to Capture Psychological RepresentationsCode0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis—0
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object RecognitionCode0
Feature Descriptors for Tracking by Detection: a Benchmark—0
Learning to Recognize Objects by Retaining other Factors of Variation—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