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 201225 of 2042 papers

TitleStatusHype
Learning what and where to attendCode1
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling TransferCode1
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNsCode1
Doubly Right Object Recognition: A Why Prompt for Visual RationalesCode1
Harmonizing the object recognition strategies of deep neural networks with humansCode1
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation LearningCode1
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual DependencyCode1
Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object DetectorsCode1
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
Implicit Feature Refinement for Instance SegmentationCode1
Learning Counterfactually Invariant PredictorsCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
Equalization Loss for Long-Tailed Object RecognitionCode1
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge DistillationCode1
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking ConsistencyCode1
Evolving Deep Neural NetworksCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
A Study of Face Obfuscation in ImageNetCode1
MomentumSMoE: Integrating Momentum into Sparse Mixture of ExpertsCode1
GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in DarkCode0
A Dataset for Crucial Object Recognition in Blind and Low-Vision Individuals' NavigationCode0
A Multi-viewpoint Outdoor Dataset for Human Action RecognitionCode0
Generalisation in humans and deep neural networksCode0
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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