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 151–200 of 2042 papers

TitleStatusHype
Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image ClassificationCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Harmonizing the object recognition strategies of deep neural networks with humansCode1
Lidar Annotation Is All You NeedCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
Li-ion battery degradation modes diagnosis via Convolutional Neural NetworksCode1
Computing the Testing Error without a Testing SetCode1
Computing the Testing Error Without a Testing SetCode1
Attribution in Scale and SpaceCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Convolutional Neural Networks with Gated Recurrent ConnectionsCode1
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy PredictionCode1
Learning what and where to attendCode1
A Study of Face Obfuscation in ImageNetCode1
Microsoft COCO: Common Objects in ContextCode1
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual GroundingCode1
DaWin: Training-free Dynamic Weight Interpolation for Robust AdaptationCode1
Debiased Self-Training for Semi-Supervised LearningCode1
Decoding Natural Images from EEG for Object RecognitionCode1
Multiple Instance Detection Network with Online Instance Classifier RefinementCode1
Full-Glow: Fully conditional Glow for more realistic image generationCode1
Noise or Signal: The Role of Image Backgrounds in Object RecognitionCode1
OBBStacking: An Ensemble Method for Remote Sensing Object DetectionCode1
ObjectNet Dataset: Reanalysis and CorrectionCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
When and how CNNs generalize to out-of-distribution category-viewpoint combinationsCode1
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningCode1
On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic SurveyCode1
Going Deeper with ConvolutionsCode1
OverFeat: Integrated Recognition, Localization and Detection using Convolutional NetworksCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
From Chaos Comes Order: Ordering Event Representations for Object Recognition and DetectionCode1
PartImageNet++ Dataset: Scaling up Part-based Models for Robust RecognitionCode1
Describing Textures in the WildCode1
DesCo: Learning Object Recognition with Rich Language DescriptionsCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Rehearsal-Free Continual Learning over Small Non-I.I.D. BatchesCode1
Paxion: Patching Action Knowledge in Video-Language Foundation ModelsCode1
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object DetectionCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
Distributed Deep Neural Networks over the Cloud, the Edge and End DevicesCode1
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing ImageryCode1
Do Adversarially Robust ImageNet Models Transfer Better?Code1
Relation Networks for Object DetectionCode1
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance SegmentationCode1
DOCTOR: A Simple Method for Detecting Misclassification ErrorsCode1
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object LearningCode1
MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object DetectionCode1
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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