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 851–900 of 2042 papers

TitleStatusHype
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
EZSR: Event-based Zero-Shot Recognition—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
Global Deconvolutional Networks for Semantic Segmentation—0
A Real-time Junk Food Recognition System based on Machine Learning—0
Going Deeper into Action Recognition: A Survey—0
Extreme Image Transformations Affect Humans and Machines Differently—0
Gradient-based Laplacian Feature Selection—0
Gradients of Counterfactuals—0
Exponential Discriminative Metric Embedding in Deep Learning—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
Label Efficient Regularization and Propagation for Graph Node Classification—0
Graphical Gaussian Vector for Image Categorization—0
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Grassmannian learning mutual subspace method for image set recognition—0
Are Accuracy and Robustness Correlated?—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Exploration of object recognition from 3D point cloud—0
Guided SAM: Label-Efficient Part Segmentation—0
GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
A randomized gradient-free attack on ReLU networks—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
Hand-Object Interaction and Precise Localization in Transitive Action Recognition—0
Hand-Priming in Object Localization for Assistive Egocentric Vision—0
Exploiting Temporal Relations on Radar Perception for Autonomous Driving—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation—0
A Random-Fern based Feature Approach for Image Matching—0
A Framework For Refining Text Classification and Object Recognition from Academic Articles—0
A concatenating framework of shortcut convolutional neural networks—0
Hebbian Semi-Supervised Learning in a Sample Efficiency Setting—0
HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems—0
HFirst: A Temporal Approach to Object Recognition—0
3D Object Recognition with Deep Belief Nets—0
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Hierarchical Deep Learning Architecture For 10K Objects Classification—0
Exploit Bounding Box Annotations for Multi-label Object Recognition—0
Hierarchically Compositional Tasks and Deep Convolutional Networks—0
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—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