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 951–1000 of 2042 papers

TitleStatusHype
Noise or Signal: The Role of Image Backgrounds in Object RecognitionCode1
Interpretable multimodal fusion networks reveal mechanisms of brain cognition—0
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
v2e: From Video Frames to Realistic DVS EventsCode1
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
Image Enhancement and Object Recognition for Night Vision Surveillance—0
What takes the brain so long: Object recognition at the level of minimal images develops for up to seconds of presentation time—0
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks—0
Training Deep Spiking Neural Networks—0
Information Mandala: Statistical Distance Matrix with Clustering—0
Anomaly Detection with Domain Adaptation—0
2D Image Features Detector And Descriptor Selection Expert System—0
Adaptive Subspaces for Few-Shot LearningCode1
Computing the Testing Error Without a Testing SetCode1
Recognizing Objects From Any View With Object and Viewer-Centered Representations—0
Traditional Method Inspired Deep Neural Network for Edge DetectionCode1
Adversarial Attacks and Defense on Texts: A Survey—0
Object-QA: Towards High Reliable Object Quality Assessment—0
End-to-End Auditory Object Recognition via Inception Nucleus—0
Misalignment Resilient Diffractive Optical Networks—0
Human-like general language processing—0
Visual Relationship Detection using Scene Graphs: A Survey—0
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
How Can CNNs Use Image Position for Segmentation?—0
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal—0
Immersive Language Exploration with Object Recognition and Augmented Reality—0
Computing the Testing Error without a Testing SetCode1
Deflating Dataset Bias Using Synthetic Data Augmentation—0
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report—0
When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene RecognitionCode1
A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition—0
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation—0
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action RecognitionCode1
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network AttributionCode1
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object RecognitionCode0
Five Points to Check when Comparing Visual Perception in Humans and MachinesCode0
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All CircuitsCode1
Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs—0
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People DetectionCode0
CAggNet: Crossing Aggregation Network for Medical Image Segmentation—0
Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB ImagesCode0
MotionRec: A Unified Deep Framework for Moving Object Recognition—0
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection SystemsCode1
Feature Pyramid GridsCode0
Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object DetectorsCode1
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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