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 926–950 of 2042 papers

TitleStatusHype
Exploit Bounding Box Annotations for Multi-label Object Recognition—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Human peripheral blur is optimal for object recognition—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
Humans and deep networks largely agree on which kinds of variation make object recognition harder—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Human vs. Computer in Scene and Object Recognition—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
Hybrid Predictive Coding: Inferring, Fast and Slow—0
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Hyper-parameter optimization of Deep Convolutional Networks for object recognition—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations—0
Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition—0
A Framework for Multi-View Classification of Features—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Identifying Object States in Cooking-Related Images—0
Identity documents recognition and detection using semantic segmentation with convolutional neural network—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal—0
Combined Approach for Image Segmentation—0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment—0
Combinatorial clustering and the beta negative binomial process—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