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 1901–1925 of 2042 papers

TitleStatusHype
Development of Open Informal Dataset Affecting Autonomous Driving—0
DeViSE: A Deep Visual-Semantic Embedding Model—0
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
DipMe: Haptic Recognition of Granular Media for Tangible Interactive Applications—0
Direct Object Recognition Without Line-of-Sight Using Optical Coherence—0
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency—0
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets—0
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning—0
Discriminative Ferns Ensemble for Hand Pose Recognition—0
Discriminatively Trained Sparse Code Gradients for Contour Detection—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Disentangling Properties of Contrastive Methods—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
Diversity in Object Proposals—0
DNN Quantization with Attention—0
Does Object Recognition Work for Everyone?—0
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?—0
Domain Adaptation on the Statistical Manifold—0
Domain Adaptive Neural Networks for Object Recognition—0
Domain Generalization: A Survey—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