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 426450 of 2042 papers

TitleStatusHype
Object recognition in atmospheric turbulence scenesCode0
Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based RecognitionCode0
Occlusion Coherence: Detecting and Localizing Occluded FacesCode0
Experiments with mmWave Automotive Radar Test-bedCode0
CORe50: a New Dataset and Benchmark for Continuous Object RecognitionCode0
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving ObjectCode0
BViT: Broad Attention based Vision TransformerCode0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object RecognitionCode0
End-to-End Learning of Representations for Asynchronous Event-Based DataCode0
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep LearningCode0
OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair NavigationCode0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
Ensemble learning in CNN augmented with fully connected subnetworksCode0
Dynamic Rectification Knowledge DistillationCode0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural NetworkCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric LearningCode0
Don't Judge by the Look: Towards Motion Coherent Video RepresentationCode0
Training Deep Neural Networks via Branch-and-BoundCode0
Do Pre-trained Vision-Language Models Encode Object States?Code0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
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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