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 601–650 of 2042 papers

TitleStatusHype
Reason from Context with Self-supervised Learning—0
The purpose of qualia: What if human thinking is not (only) information processing?—0
R2-MLP: Round-Roll MLP for Multi-View 3D Object RecognitionCode0
Adversarial Detection by Approximation of Ensemble Boundary—0
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data—0
Egocentric Audio-Visual Noise Suppression—0
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks—0
State-of-the-art Models for Object Detection in Various Fields of Application—0
Subsidiary Prototype Alignment for Universal Domain Adaptation—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
Object recognition in atmospheric turbulence scenesCode0
Context-driven Visual Object Recognition based on Knowledge Graphs—0
Object Recognition in Different Lighting Conditions at Various Angles by Deep Learning Method—0
Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models—0
Variant Parallelism: Lightweight Deep Convolutional Models for Distributed Inference on IoT Devices—0
Hand Gestures Recognition in Videos Taken with Lensless Camera—0
Learning by Asking Questions for Knowledge-based Novel Object Recognition—0
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping—0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition—0
Multipod Convolutional Network—0
RECALL: Rehearsal-free Continual Learning for Object ClassificationCode0
Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking—0
Reconstruction-guided attention improves the robustness and shape processing of neural networksCode0
Transformer-Based Microbubble Localization—0
TANDEM3D: Active Tactile Exploration for 3D Object Recognition—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Continual Learning for Pose-Agnostic Object Recognition in 3D Point Clouds—0
Measuring Human Perception to Improve Open Set Recognition—0
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer—0
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method—0
Chosen methods of improving small object recognition with weak recognizable features—0
A Simulation Method for MMW Radar Sensing in Traffic Intersection Based on BART Algorithm—0
How good are deep models in understanding the generated images?—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Noise-Adaptive Intelligent Programmable Meta-Imager—0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Background Invariance Testing According to Semantic Proximity—0
Modeling biological face recognition with deep convolutional neural networks—0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
A neuromorphic approach to image processing and machine vision—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Easy Batch Normalization—0
Semi-supervised Ranking for Object Image Blur AssessmentCode0
A Survey of Task-Based Machine Learning Content Extraction Services for VIDINT—0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Multi-area Target Individual Detection with Free Drawing on VideoCode0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images—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