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 651–675 of 2042 papers

TitleStatusHype
Efficient Codebook and Factorization for Second Order Representation Learning—0
Efficient Estimation of Regularized Tyler's M-Estimator Using Approximate LOOCV—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks—0
A neuromorphic approach to image processing and machine vision—0
Efficient Image Categorization with Sparse Fisher Vector—0
Efficient Multi-Band Temporal Video Filter for Reducing Human-Robot Interaction—0
Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition—0
Efficient Oriented Object Detection with Enhanced Small Object Recognition in Aerial Images—0
Efficient Point-to-Subspace Query in ^1 with Application to Robust Object Instance Recognition—0
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition—0
Egocentric Audio-Visual Noise Suppression—0
Egocentric Height Estimation—0
Egocentric Hierarchical Visual Semantics—0
EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision—0
Advancing Egocentric Video Question Answering with Multimodal Large Language Models—0
Eigen-Distortions of Hierarchical Representations—0
EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More—0
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition—0
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices—0
Embodied vision for learning object representations—0
Emergent communication for AR—0
EmoCAM: Toward Understanding What Drives CNN-based Emotion Recognition—0
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning—0
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching—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