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 301–350 of 2042 papers

TitleStatusHype
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation LearningCode1
Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions for Assembly—0
Polyhedral Object Recognition by Indexing—0
DAS: A Deformable Attention to Capture Salient Information in CNNs—0
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method—0
Partial Coherence for Object Recognition and Depth Sensing—0
Lidar Annotation Is All You NeedCode1
Selective Visual Representations Improve Convergence and Generalization for Embodied AI—0
Dataset for flood area recognition with semantic segmentation—0
Open-Set Object Recognition Using Mechanical Properties During Interaction—0
Recognize Any RegionsCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-endsCode1
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations—0
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configurationCode0
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges—0
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis—0
Intriguing properties of generative classifiersCode1
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time—0
Recursive Counterfactual Deconfounding for Object Recognition—0
Motion Segmentation from a Moving Monocular Camera—0
Algorithms for Object Detection in Substations—0
Edge Aware Learning for 3D Point Cloud—0
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object RecognitionCode1
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments—0
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks—0
RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline—0
Grounded Language Acquisition From Object and Action Imagery—0
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-ArtCode1
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception—0
Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNNCode1
Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data—0
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection—0
Modeling infant object perception as program induction—0
Graph-based Asynchronous Event Processing for Rapid Object Recognition—0
Decoding Natural Images from EEG for Object RecognitionCode1
Characterising representation dynamics in recurrent neural networks for object recognition—0
End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions—0
Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from EventsCode1
BSED: Baseline Shapley-Based Explainable Detector—0
Emergent communication for AR—0
Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs—0
Scaling may be all you need for achieving human-level object recognition capacity with human-like visual experienceCode1
Surface Defects Detection of Transparent Plastic Bottles Based on Improved Yolov5—0
A semantics-driven methodology for high-quality image annotation—0
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?—0
Online Continual Learning for Robust Indoor Object Recognition—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