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

Showing 126150 of 2042 papers

TitleStatusHype
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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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified