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 1151–1200 of 2042 papers

TitleStatusHype
(RF)^2 -- Random Forest Random Field—0
RIDE: Reversal Invariant Descriptor Enhancement—0
RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
RMNv2: Reduced Mobilenet V2 for CIFAR10—0
Robot In a Room: Toward Perfect Object Recognition in Closed Environments—0
Robust Domain Generalization for Multi-modal Object Recognition—0
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras—0
Robustness of Humans and Machines on Object Recognition with Extreme Image Transformations—0
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers—0
Robust Shape Regularity Criteria for Superpixel Evaluation—0
Robust Visual Knowledge Transfer via EDA—0
Rock Hunting With Martian Machine Vision—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition—0
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Rotation-invariant shipwreck recognition with forward-looking sonar—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks—0
Saliency Driven Object recognition in egocentric videos with deep CNN—0
Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data—0
Saliency for free: Saliency prediction as a side-effect of object recognition—0
Salient Explanation for Fine-grained Classification—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
Salt Detection Using Segmentation of Seismic Image—0
Sample Balancing for Improving Generalization under Distribution Shifts—0
SCaLE: Supervised and Cascaded Laplacian Eigenmaps for Visual Object Recognition Based on Nearest Neighbors—0
Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs—0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization—0
ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification—0
Scenarios: A New Representation for Complex Scene Understanding—0
Scene Labeling with Contextual Hierarchical Models—0
Scene Parsing by Integrating Function, Geometry and Appearance Models—0
Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning—0
SdcNet: A Computation-Efficient CNN for Object Recognition—0
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation—0
Seeing What's Not There: Spurious Correlation in Multimodal LLMs—0
See No Evil, Say No Evil: Description Generation from Densely Labeled Images—0
"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models—0
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing—0
Selective Segmentation Networks Using Top-Down Attention—0
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)—0
Selective Visual Representations Improve Convergence and Generalization for Embodied AI—0
Self-Adaptable Templates for Feature Coding—0
Self-informed neural network structure learning—0
Self-Supervised Learning Across Domains—0
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects—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