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 1301–1350 of 2042 papers

TitleStatusHype
The Effect of Top-Down Attention in Occluded Object Recognition—0
The Effects of Image Distribution and Task on Adversarial Robustness—0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions—0
The functional role of cue-driven feature-based feedback in object recognition—0
The iCub multisensor datasets for robot and computer vision applications—0
The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection—0
The Image Torque Operator for Contour Processing—0
The Labeled Multiple Canonical Correlation Analysis for Information Fusion—0
The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations—0
The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units—0
The Neural Correlates of Image Texture in the Human Vision Using Magnetoencephalography—0
The Newton Scheme for Deep Learning—0
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects—0
The purpose of qualia: What if human thinking is not (only) information processing?—0
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates—0
The Ripple Pond: Enabling Spiking Networks to See—0
The role of temporal cortex in the control of attention—0
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks—0
The same but different: impact of animal facility sanitary status on a transgenic mouse model of Alzheimer's disease—0
The Semantics of Image Annotation—0
The Toybox Dataset of Egocentric Visual Object Transformations—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.—0
Thinning Algorithm Using Hypergraph Based Morphological Operators—0
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition—0
Three Guidelines of Online Learning for Large-Scale Visual Recognition—0
Through-Wall Object Recognition and Pose Estimation—0
Tiled convolutional neural networks—0
Timely Object Recognition—0
TKD: Temporal Knowledge Distillation for Active Perception—0
To Boost or not to Boost: On the Limits of Boosted Neural Networks—0
Top-Down Regularization of Deep Belief Networks—0
Topologically-Guided Color Image Enhancement—0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments—0
Topology Maintained Structure Encoding—0
Toward a Geometric Theory of Manifold Untangling—0
Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis—0
Towards Bayesian Deep Learning: A Framework and Some Existing Methods—0
Applications of knowledge graphs for food science and industry—0
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition—0
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation—0
Towards ontology driven learning of visual concept detectors—0
Towards Open World Recognition—0
Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors—0
Towards real-time object recognition and pose estimation in point clouds—0
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation—0
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition—0
Towards Zero-Shot & Explainable Video Description by Reasoning over Graphs of Events in Space and Time—0
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition—0
TrackVLA: Embodied Visual Tracking in the Wild—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