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 1851–1900 of 2042 papers

TitleStatusHype
Causal importance of orientation selectivity for generalization in image recognitionCode0
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media ContextsCode0
UAVid: A Semantic Segmentation Dataset for UAV ImageryCode0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternativesCode0
Directional Regularized Tensor Modeling for Video Rain Streaks RemovalCode0
Infinite Feature SelectionCode0
Semantically Meaningful View SelectionCode0
Quaternion Capsule NetworksCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Semantic Edge Detection with Diverse Deep SupervisionCode0
R2-MLP: Round-Roll MLP for Multi-View 3D Object RecognitionCode0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Feature Pyramid GridsCode0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
Visual Object Recognition in Indoor Environments Using Topologically Persistent FeaturesCode0
Feature Learning for Accelerometer based Gait RecognitionCode0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
Detecting semantic anomaliesCode0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels MethodsCode0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Interpreting Adversarially Trained Convolutional Neural NetworksCode0
Attention Based Pruning for Shift NetworksCode0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Feature Learning by Multidimensional Scaling and its Applications in Object RecognitionCode0
Intrinsic dimension estimation for locally undersampled dataCode0
A Computational Acquisition Model for Multimodal Word CategorizationCode0
Analysis and Optimization of Convolutional Neural Network ArchitecturesCode0
Semi-supervised Ranking for Object Image Blur AssessmentCode0
ViSTa Dataset: Do vision-language models understand sequential tasks?Code0
A Multi-viewpoint Outdoor Dataset for Human Action RecognitionCode0
Investigating Negation in Pre-trained Vision-and-language ModelsCode0
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural NetworksCode0
Real Classification by Description: Extending CLIP's Limits of Part Attributes RecognitionCode0
Investigating the Nature of 3D Generalization in Deep Neural NetworksCode0
Target-Aware Generative Augmentations for Single-Shot AdaptationCode0
Targeted View-Invariant Adversarial Perturbations for 3D Object RecognitionCode0
Real-Time Correlation Tracking via Joint Model Compression and TransferCode0
Dense and Diverse Capsule Networks: Making the Capsules Learn BetterCode0
Fast Feature Fool: A data independent approach to universal adversarial perturbationsCode0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Seq-NMS for Video Object DetectionCode0
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
Is Second-order Information Helpful for Large-scale Visual Recognition?Code0
Associative Alignment for Few-shot Image ClassificationCode0
Delta-encoder: an effective sample synthesis method for few-shot object recognitionCode0
Faster gaze prediction with dense networks and Fisher pruningCode0
RECALL: Rehearsal-free Continual Learning for Object ClassificationCode0
Kernel Manifold AlignmentCode0
Recent Advances in Neural Program SynthesisCode0
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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