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 1951–2000 of 2042 papers

TitleStatusHype
EdgeOL: Efficient in-situ Online Learning on Edge Devices—0
Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environments—0
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model—0
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Efficient Codebook and Factorization for Second Order Representation Learning—0
Efficient Estimation of Regularized Tyler's M-Estimator Using Approximate LOOCV—0
Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks—0
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures—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
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
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Empowering Local Communities Using Artificial Intelligence—0
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash—0
Encoder-Decoder based CNN and Fully Connected CRFs for Remote Sensed Image Segmentation—0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors—0
End-to-End Auditory Object Recognition via Inception Nucleus—0
End-to-end Binary Representation Learning via Direct Binary Embedding—0
End-to-End Race Driving with Deep Reinforcement Learning—0
End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions—0
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random—0
Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs—0
Energy Efficient Hadamard Neural Networks—0
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis—0
Enhancing Crime Scene Investigations through Virtual Reality and Deep Learning Techniques—0
Enhancing efficiency of object recognition in different categorization levels by reinforcement learning in modular spiking neural networks—0
Enhancing Object Detection in Adverse Conditions using Thermal Imaging—0
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection—0
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation—0
Enlightening Deep Neural Networks with Knowledge of Confounding Factors—0
Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition—0
Estimating Bicycle Route Attractivity from Image Data—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