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 326–350 of 2042 papers

TitleStatusHype
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