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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–1000 of 2042 papers

TitleStatusHype
Joint Deep Learning for Car Detection—0
Image Enhancement and Object Recognition for Night Vision Surveillance—0
Language Models as Zero-shot Visual Semantic Learners—0
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning—0
ImageNet MPEG-7 Visual Descriptors - Technical Report—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—0
ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid—0
Image Optimization and Prediction—0
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency—0
BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
Amodal Completion and Size Constancy in Natural Scenes—0
Imaging-free object recognition enabled by optical coherence—0
Immersive Language Exploration with Object Recognition and Augmented Reality—0
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis—0
I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data—0
Improved Deep Learning of Object Category using Pose Information—0
Improved Deep Metric Learning with Multi-class N-pair Loss Objective—0
Improved Few-Shot Visual Classification—0
Improved Inception-Residual Convolutional Neural Network for Object Recognition—0
Improved training of binary networks for human pose estimation and image recognition—0
Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Automatically Discovering Local Visual Material Attributes—0
Interpretable Graph Capsule Networks for Object Recognition—0
Direct Object Recognition Without Line-of-Sight Using Optical Coherence—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Gibbs Sampler Scan Quality with DoGS—0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
Boosting with Maximum Adaptive Sampling—0
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study—0
Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans—0
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks—0
DipMe: Haptic Recognition of Granular Media for Tangible Interactive Applications—0
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer—0
Deep Learning for the Classification of Lung Nodules—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects—0
Boosting Object Recognition in Point Clouds by Saliency Detection—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
Incorporating Semantic Attention in Video Description Generation—0
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection—0
Incorporating Textual Evidence in Visual Storytelling—0
Incremental Learning for Robot Perception through HRI—0
Deep Learning with Energy-efficient Binary Gradient Cameras—0
Inducing Functions through Reinforcement Learning without Task Specification—0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19—0
ADLDA: A Method to Reduce the Harm of Data Distribution Shift in Data Augmentation—0
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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