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 1151–1175 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
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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