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

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Showing 101–150 of 2042 papers

TitleStatusHype
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNsCode1
Learning Counterfactually Invariant PredictorsCode1
Do Adversarially Robust ImageNet Models Transfer Better?Code1
Distributed Deep Neural Networks over the Cloud, the Edge and End DevicesCode1
DOCTOR: A Simple Method for Detecting Misclassification ErrorsCode1
Lidar Annotation Is All You NeedCode1
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking ConsistencyCode1
Li-ion battery degradation modes diagnosis via Convolutional Neural NetworksCode1
Describing Textures in the WildCode1
Look-into-Object: Self-supervised Structure Modeling for Object RecognitionCode1
Microsoft COCO: Common Objects in ContextCode1
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual GroundingCode1
MomentumSMoE: Integrating Momentum into Sparse Mixture of ExpertsCode1
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworksCode1
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual CortexCode1
N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event CamerasCode1
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningCode1
OBBStacking: An Ensemble Method for Remote Sensing Object DetectionCode1
Offline Meta-Reinforcement Learning with Advantage WeightingCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
Adaptive Subspaces for Few-Shot LearningCode1
On the Challenges of Open World Recognitionunder Shifting Visual DomainsCode1
Adaptive Threshold for Online Object Recognition and Re-identification TasksCode1
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
OverFeat: Integrated Recognition, Localization and Detection using Convolutional NetworksCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
Equalization Loss for Long-Tailed Object RecognitionCode1
Debiased Self-Training for Semi-Supervised LearningCode1
CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal DynamicsCode1
Decoding Natural Images from EEG for Object RecognitionCode1
Convolutional Neural Networks with Gated Recurrent ConnectionsCode1
Computing the Testing Error without a Testing SetCode1
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object DetectionCode1
Computing the Testing Error Without a Testing SetCode1
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy PredictionCode1
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNetCode1
Contemplating real-world object classificationCode1
Contributions of Shape, Texture, and Color in Visual RecognitionCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessmentCode1
Causal Transportability for Visual RecognitionCode1
DaWin: Training-free Dynamic Weight Interpolation for Robust AdaptationCode1
CLoVe: Encoding Compositional Language in Contrastive Vision-Language ModelsCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
Densely Connected Convolutional NetworksCode1
DesCo: Learning Object Recognition with Rich Language DescriptionsCode1
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object DetectionCode1
Discover and Cure: Concept-aware Mitigation of Spurious CorrelationCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in VideoCode1
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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