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 151–200 of 2042 papers

TitleStatusHype
Billion-scale semi-supervised learning for image classificationCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
Lidar Annotation Is All You NeedCode1
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022Code1
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object DetectionCode1
Computing the Testing Error Without a Testing SetCode1
Contemplating real-world object classificationCode1
Convolutional Neural Networks with Gated Recurrent ConnectionsCode1
Contributions of Shape, Texture, and Color in Visual RecognitionCode1
CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal DynamicsCode1
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy PredictionCode1
Attribution in Scale and SpaceCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object LearningCode1
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual GroundingCode1
A Study of Face Obfuscation in ImageNetCode1
Multiple Instance Detection Network with Online Instance Classifier RefinementCode1
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual CortexCode1
Decoding Natural Images from EEG for Object RecognitionCode1
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNetCode1
Full-Glow: Fully conditional Glow for more realistic image generationCode1
ObjectNet Dataset: Reanalysis and CorrectionCode1
Object Recognition as Next Token PredictionCode1
Discover and Cure: Concept-aware Mitigation of Spurious CorrelationCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic SurveyCode1
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group SoftmaxCode1
Learning what and where to attendCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
From Chaos Comes Order: Ordering Event Representations for Object Recognition and DetectionCode1
Rehearsal-Free Continual Learning over Small Non-I.I.D. BatchesCode1
PartImageNet++ Dataset: Scaling up Part-based Models for Robust RecognitionCode1
Describing Textures in the WildCode1
Are Convolutional Neural Networks or Transformers more like human vision?Code1
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object DetectionCode1
Causal Transportability for Visual RecognitionCode1
Paxion: Patching Action Knowledge in Video-Language Foundation ModelsCode1
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object RecognitionCode1
Recognize Any RegionsCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Distributed Deep Neural Networks over the Cloud, the Edge and End DevicesCode1
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance SegmentationCode1
DOCTOR: A Simple Method for Detecting Misclassification ErrorsCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Robust and efficient post-processing for video object detectionCode1
Going Deeper with ConvolutionsCode1
MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object DetectionCode1
Show:102550
← PrevPage 4 of 41Next →
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