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 1451–1475 of 2042 papers

TitleStatusHype
Memory Aware Synapses: Learning what (not) to forgetCode0
Context Augmentation for Convolutional Neural Networks—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—0
ADVISE: Symbolism and External Knowledge for Decoding Advertisements—0
Robust Unsupervised Domain Adaptation for Neural Networks via Moment AlignmentCode0
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled HolesCode0
Latent Constrained Correlation Filter—0
Analysis of Dropout in Online Learning—0
Interpreting Convolutional Neural Networks Through Compression—0
Few-Shot Adversarial Domain Adaptation—0
Procedural Text Generation from an Execution Video—0
Cascade Region Proposal and Global Context for Deep Object Detection—0
On Pre-Trained Image Features and Synthetic Images for Deep Learning—0
Object Recognition by Using Multi-level Feature Point Extraction—0
Classification and Geometry of General Perceptual Manifolds—0
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields—0
Graph Convolutional Networks for Classification with a Structured Label Space—0
Eigen-Distortions of Hierarchical Representations—0
A concatenating framework of shortcut convolutional neural networks—0
Deep Scene Image Classification With the MFAFVNet—0
Understanding Low- and High-Level Contributions to Fixation Prediction—0
Deep Competitive Pathway NetworksCode0
Are we done with object recognition? The iCub robot's perspectiveCode0
A Generic Regression Framework for Pose Recognition on Color and Depth Images—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