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 726–750 of 2042 papers

TitleStatusHype
Combined Approach for Image Segmentation—0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Exploit Bounding Box Annotations for Multi-label Object Recognition—0
Feature Space Transfer for Data Augmentation—0
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
A randomized gradient-free attack on ReLU networks—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection—0
Exponential Discriminative Metric Embedding in Deep Learning—0
Extreme Image Transformations Affect Humans and Machines Differently—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
EZSR: Event-based Zero-Shot Recognition—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Disentangling Properties of Contrastive 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