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 1651–1675 of 2042 papers

TitleStatusHype
Improved Deep Learning of Object Category using Pose Information—0
FusionNet: 3D Object Classification Using Multiple Data Representations—0
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
Do semantic parts emerge in Convolutional Neural Networks?—0
From Dependence to Causation—0
Deep Reconstruction-Classification Networks for Unsupervised Domain AdaptationCode0
Enlightening Deep Neural Networks with Knowledge of Confounding Factors—0
Captioning Images with Diverse ObjectsCode0
Saliency Driven Object recognition in egocentric videos with deep CNN—0
Mutual Exclusivity Loss for Semi-Supervised Deep Learning—0
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)—0
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation—0
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?—0
How Deep is the Feature Analysis underlying Rapid Visual Categorization?—0
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
Learning compact binary descriptors with unsupervised deep neural networksCode0
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition—0
Interactive Segmentation on RGBD Images via Cue Selection—0
Occlusion Boundary Detection via Deep Exploration of Context—0
BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition—0
Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks—0
Modality and Component Aware Feature Fusion For RGB-D Scene Classification—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