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
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?—0
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation—0
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities—0
How Deep is the Feature Analysis underlying Rapid Visual Categorization?—0
Learning compact binary descriptors with unsupervised deep neural networksCode0
Modality and Component Aware Feature Fusion For RGB-D Scene Classification—0
Image Style Transfer Using Convolutional Neural NetworksCode0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Interactive Segmentation on RGBD Images via Cue Selection—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition—0
Occlusion Boundary Detection via Deep Exploration of Context—0
Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks—0
Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent—0
Answer-Type Prediction for Visual Question Answering—0
Consistency of Silhouettes and Their Duals—0
Pairwise Linear Regression Classification for Image Set Retrieval—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition—0
Latent Bi-constraint SVM for Video-based Object Recognition—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
Towards ontology driven learning of visual concept detectors—0
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval—0
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks—0
Semi-supervised Zero-Shot Learning by a Clustering-based Approach—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