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 1376–1400 of 2042 papers

TitleStatusHype
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash—0
Multi-level 3D CNN for Learning Multi-scale Spatial FeaturesCode0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-OrganizationCode0
Deep Watershed Detector for Music Object Recognition—0
Learning Illuminant Estimation from Object Recognition—0
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching—0
Deformable Part Networks—0
Object Localization with a Weakly Supervised CapsNet—0
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Disparity Sliding Window: Object Proposals From Disparity ImagesCode0
Identifying Object States in Cooking-Related Images—0
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation—0
Energy Efficient Hadamard Neural Networks—0
Dense and Diverse Capsule Networks: Making the Capsules Learn BetterCode0
SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
SdcNet: A Computation-Efficient CNN for Object Recognition—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Incorporating Semantic Attention in Video Description Generation—0
Semi-supervised Training Data Generation for Multilingual Question Answering—0
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving ObjectsCode0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
Semantic Edge Detection with Diverse Deep SupervisionCode0
Performance Evaluation of 3D Correspondence Grouping Algorithms—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