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

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Showing 1001–1050 of 2042 papers

TitleStatusHype
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition—0
Textured As-Is BIM via GIS-informed Point Cloud Segmentation—0
Texture image analysis and texture classification methods - A review—0
Textureless Object Recognition: An Edge-based Approach—0
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences—0
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition—0
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks—0
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks—0
The Distribution Family of Similarity Distances—0
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs—0
The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance—0
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
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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