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 1801–1850 of 2042 papers

TitleStatusHype
Convolutional Models for Joint Object Categorization and Pose Estimation—0
Convolutional Networks with Dense Connectivity—0
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future—0
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects—0
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling—0
Convolutional Prototype Learning for Zero-Shot Recognition—0
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks—0
Convolutional Tables Ensemble: classification in microseconds—0
Co-occurrence matrix analysis-based semi-supervised training for object detection—0
Cooking Object's State Identification Without Using Pretrained Model—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition—0
CortexNet: a Generic Network Family for Robust Visual Temporal Representations—0
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation—0
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
Counting the learnable functions of structured data—0
CPWC: Contextual Point Wise Convolution for Object Recognition—0
Crowdsourcing in Computer Vision—0
CURL: Co-trained Unsupervised Representation Learning for Image Classification—0
CVSNet: A Computer Implementation for Central Visual System of The Brain—0
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection—0
DAS: A Deformable Attention to Capture Salient Information in CNNs—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Data-Driven 3D Voxel Patterns for Object Category Recognition—0
Data-driven Verification of DNNs for Object Recognition—0
Dataset for flood area recognition with semantic segmentation—0
DaTscan SPECT Image Classification for Parkinson's Disease—0
DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability—0
DCI: Discriminative and Contrast Invertible Descriptor—0
DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects—0
Deep Active Object Recognition by Joint Label and Action Prediction—0
Deep Affordance-grounded Sensorimotor Object Recognition—0
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks—0
Deep Boosting of Diverse Experts—0
Deep Clustering by Semantic Contrastive Learning—0
DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection—0
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics—0
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
Deep Global-Connected Net With The Generalized Multi-Piecewise ReLU Activation in Deep Learning—0
Deep Graph Reprogramming—0
DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING—0
Deep Learning and Continuous Representations for Natural Language Processing—0
Deep Learning and Music Adversaries—0
Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds—0
Deep learning based infrared small object segmentation: Challenges and future directions—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Deep Learning for the Classification of Lung Nodules—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