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 126–150 of 2042 papers

TitleStatusHype
ChartKG: A Knowledge-Graph-Based Representation for Chart Images—0
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts—0
DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric LearningCode0
MVP-Bench: Can Large Vision--Language Models Conduct Multi-level Visual Perception Like Humans?Code0
Fast Object Detection with a Machine Learning Edge Device—0
DaWin: Training-free Dynamic Weight Interpolation for Robust AdaptationCode1
CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessmentCode1
Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility ConditionsCode0
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer—0
You Only Speak Once to See—0
Enhancing Crime Scene Investigations through Virtual Reality and Deep Learning Techniques—0
AI-Powered Augmented Reality for Satellite Assembly, Integration and Test—0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Formula-Supervised Visual-Geometric Pre-training—0
EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism—0
Benchmarking VLMs' Reasoning About Persuasive Atypical Images—0
Do Pre-trained Vision-Language Models Encode Object States?Code0
Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based RecognitionCode0
Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory AnnotationsCode0
Generalization Boosted Adapter for Open-Vocabulary Segmentation—0
Performance Assessment of Feature Detection Methods for 2-D FS Sonar Imagery—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels—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