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 101150 of 2042 papers

TitleStatusHype
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNsCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance SegmentationCode1
Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-endsCode1
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNetCode1
Microsoft COCO: Common Objects in ContextCode1
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking ConsistencyCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningCode1
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworksCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object DetectionCode1
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing ImageryCode1
Noise or Signal: The Role of Image Backgrounds in Object RecognitionCode1
From Chaos Comes Order: Ordering Event Representations for Object Recognition and DetectionCode1
Densely Connected Convolutional NetworksCode1
Describing Textures in the WildCode1
ObjectNet Dataset: Reanalysis and CorrectionCode1
Discover and Cure: Concept-aware Mitigation of Spurious CorrelationCode1
Learning what and where to attendCode1
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
ImageNet Large Scale Visual Recognition ChallengeCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in VideoCode1
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
Equalization Loss for Long-Tailed Object RecognitionCode1
Attribution in Scale and SpaceCode1
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge DistillationCode1
Evolving Deep Neural NetworksCode1
Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detectionCode1
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
Event-based Asynchronous Sparse Convolutional NetworksCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Billion-scale semi-supervised learning for image classificationCode1
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?Code1
Ev-TTA: Test-Time Adaptation for Event-Based Object RecognitionCode1
Exploring the Transferability of Visual Prompting for Multimodal Large Language ModelsCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image ClassificationCode1
Rehearsal-Free Continual Learning over Small Non-I.I.D. BatchesCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Causal Transportability for Visual RecognitionCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
A Study of Face Obfuscation in ImageNetCode1
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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