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 1026–1050 of 2042 papers

TitleStatusHype
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks—0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction—0
Analyzing the Dependency of ConvNets on Spatial Information—0
Selective Segmentation Networks Using Top-Down Attention—0
EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision—0
OPFython: A Python-Inspired Optimum-Path Forest ClassifierCode1
AI-Powered GUI Attack and Its Defensive Methods—0
Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving—0
Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance—0
Block-wise Scrambled Image Recognition Using Adaptation Network—0
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future—0
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration—0
Learning Transformation-Aware Embeddings for Image Forensics—0
Multi-Scale Weight Sharing Network for Image Recognition—0
RMNv2: Reduced Mobilenet V2 for CIFAR10—0
Convolutional Networks with Dense Connectivity—0
Fractional order graph neural network—0
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?Code1
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION—0
KNN-Based Automatic Cropping for Improved Threat Object Recognition in X-Ray Security Images—0
Experiments with mmWave Automotive Radar Test-bedCode0
Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset—0
Importance Filtered Cross-Domain Adaptation—0
Interactive Open-Ended Learning for 3D Object Recognition—0
Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds—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