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 751–800 of 2042 papers

TitleStatusHype
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
Face-space Action Recognition by Face-Object Interactions—0
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
Farm land weed detection with region-based deep convolutional neural networks—0
Fashioning with Networks: Neural Style Transfer to Design Clothes—0
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition—0
Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels—0
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations—0
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Compositional Embeddings for Multi-Label One-Shot Learning—0
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition—0
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations—0
Fast Object Detection with a Machine Learning Edge Device—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—0
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks—0
Fast Training of Deep Networks with One-Class CNNs—0
Feature-based Attention in Convolutional Neural Networks—0
Feature-Based Lie Group Transformer for Real-World Applications—0
Feature Descriptors for Tracking by Detection: a Benchmark—0
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement—0
A Review of methods for Textureless Object Recognition—0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
Feature Space Transfer for Data Augmentation—0
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems—0
Few-Shot Adversarial Domain Adaptation—0
Towards Contextual Learning in Few-shot Object Classification—0
Few-Shot Object Recognition from Machine-Labeled Web Images—0
Few-shot target-driven instance detection based on open-vocabulary object detection models—0
Instance Scale Normalization for image understanding—0
Filter Bank Regularization of Convolutional Neural Networks—0
Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks—0
Finding Mirror Symmetry via Registration—0
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions—0
FusionNet: 3D Object Classification Using Multiple Data Representations—0
Fine-grained 3D object recognition: an approach and experiments—0
Disentangling Properties of Contrastive Methods—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Content Placement in Networks of Similarity Caches—0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Fine-Grained Open-Vocabulary Object Recognition via User-Guided Segmentation—0
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach—0
Fine-tuning Convolutional Neural Networks for fine art classification—0
First-spike based visual categorization using reward-modulated STDP—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking—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