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 1201–1225 of 2042 papers

TitleStatusHype
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Semantic Kernel Forests from Multiple Taxonomies—0
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need—0
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer—0
Semantic Topic Analysis of Traffic Camera Images—0
Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition—0
Semi-supervised Training Data Generation for Multilingual Question Answering—0
Semi-supervised Zero-Shot Learning by a Clustering-based Approach—0
Sensitivity of sparse codes to image distortions—0
Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art—0
Sequence-based Person Attribute Recognition with Joint CTC-Attention Model—0
Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition—0
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids—0
ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching—0
Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition—0
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network—0
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching—0
Significance of feedforward architectural differences between the ventral visual stream and DenseNet—0
Simulating reaction time for Eureka effect in visual object recognition using artificial neural network—0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains—0
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition—0
Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception—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