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 1151–1200 of 2042 papers

TitleStatusHype
Neural Image Captioning—0
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial NetworkCode0
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching—0
NetTailor: Tuning the Architecture, Not Just the WeightsCode0
A shallow residual neural network to predict the visual cortex response—0
Topology Maintained Structure Encoding—0
Mutual exclusivity as a challenge for deep neural networks—0
Integrating Knowledge and Reasoning in Image Understanding—0
Intrinsic dimension estimation for locally undersampled dataCode0
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks—0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
Learning Representations of Graph Data -- A Survey—0
Does Object Recognition Work for Everyone?—0
Adaptation Across Extreme Variations using Unlabeled Domain Bridges—0
Farm land weed detection with region-based deep convolutional neural networks—0
Efficient Codebook and Factorization for Second Order Representation Learning—0
Dominant Set Clustering and Pooling for Multi-View 3D Object RecognitionCode0
Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images—0
From Virtual to Real: A Framework for Verbal Interaction with Robots—0
Weakly Supervised Image Classification Through Noise Regularization—0
Domain Generalization by Solving Jigsaw PuzzlesCode0
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition—0
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis—0
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Spatial-Aware Graph Relation Network for Large-Scale Object Detection—0
Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition—0
Attention Based Pruning for Shift NetworksCode0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
Unsupervised Learning from Video with Deep Neural EmbeddingsCode0
SpecNet: Spectral Domain Convolutional Neural Network—0
Improved object recognition using neural networks trained to mimic the brain's statistical propertiesCode0
Interpreting Adversarially Trained Convolutional Neural NetworksCode0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
Through-Wall Object Recognition and Pose Estimation—0
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth EstimationCode0
Sparse Transfer Learning via Winning Lottery TicketsCode0
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices—0
Transfer Learning based Detection of Diabetic Retinopathy from Small DatasetCode0
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search—0
VGG Fine-tuning for Cooking State Recognition—0
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational ModelsCode0
Number detectors spontaneously emerge in a deep neural network designed for visual object recognition—0
Unsupervised Domain Adaptation using Graph Transduction Games—0
Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets—0
Clustering Images by Unmasking - A New Baseline—0
Billion-scale semi-supervised learning for image classificationCode1
Localized random projections challenge benchmarks for bio-plausible deep learning—0
FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS—0
What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation—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