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

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Showing 1701–1750 of 2042 papers

TitleStatusHype
A Survey on Bayesian Deep LearningCode0
Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition—0
Deep Cross Residual Learning for Multitask Visual RecognitionCode0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
Building Machines That Learn and Think Like People—0
Object Recognition Based on Amounts of Unlabeled Data—0
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity—0
Object Recognition and Identification Using ESM Data—0
Beyond Sharing Weights for Deep Domain Adaptation—0
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures—0
Diversity in Object Proposals—0
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition—0
A Feature Learning and Object Recognition Framework for Underwater Fish Images—0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—0
Weakly Supervised Localization using Deep Feature Maps—0
Seq-NMS for Video Object DetectionCode0
The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection—0
An Approach for Noise Removal on Depth Images—0
Convolutional Tables Ensemble: classification in microseconds—0
Global Deconvolutional Networks for Semantic Segmentation—0
Unsupervised Transductive Domain Adaptation—0
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks—0
NeRD: a Neural Response Divergence Approach to Visual Salience Detection—0
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition—0
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots—0
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition—0
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
The Image Torque Operator for Contour Processing—0
Face-space Action Recognition by Face-Object Interactions—0
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition—0
Kernel principal component analysis network for image classification—0
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines—0
Deep Active Object Recognition by Joint Label and Action Prediction—0
Origami: A 803 GOp/s/W Convolutional Network Accelerator—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
What can we learn about CNNs from a large scale controlled object dataset?—0
Infinite Feature SelectionCode0
Query Adaptive Similarity Measure for RGB-D Object Recognition—0
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks—0
MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition—0
RIDE: Reversal Invariant Descriptor Enhancement—0
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition—0
Learning to Combine Mid-Level Cues for Object Proposal Generation—0
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization—0
Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos—0
Hierarchical Convolutional Features for Visual Tracking—0
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling—0
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction—0
Feature-based Attention in Convolutional Neural Networks—0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
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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