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 1651–1700 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
Pairwise Decomposition of Image Sequences for Active Multi-View Recognition—0
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningCode1
FPNN: Field Probing Neural Networks for 3D DataCode0
Hierarchical Piecewise-Constant Super-regions—0
Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery—0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Going Deeper into Action Recognition: A Survey—0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
A New Manifold Distance Measure for Visual Object Categorization—0
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels—0
Measurement Bounds for Sparse Signal Reconstruction with Multiple Side Information—0
A Theoretical Analysis of Deep Neural Networks for Texture Classification—0
Learning Attributes Equals Multi-Source Domain Generalization—0
1 Million Captioned Dutch Newspaper Images—0
Zero-shot object prediction using semantic scene knowledge—0
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration—0
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition—0
Humans and deep networks largely agree on which kinds of variation make object recognition harder—0
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks—0
Can Boosting with SVM as Week Learners Help?—0
Invariant feature extraction from event based stimuli—0
DTM: Deformable Template Matching—0
Orientation-boosted Voxel Nets for 3D Object Recognition—0
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosCode0
Edge Detection Based Shape Identification—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