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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 201–225 of 2042 papers

Title	Date	Tasks	Status	Hype
Learning what and where to attend	May 22, 2018	DiagnosticImage Categorization	CodeCode Available	1
Wavelet Convolutional Neural Networks	May 20, 2018	General Classificationimage-classification	CodeCode Available	1
Dynamic Few-Shot Visual Learning without Forgetting	Apr 25, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects	Mar 27, 2018	General ClassificationObject	CodeCode Available	1
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations	Jan 24, 2018	Adversarial AttackDepth Estimation	CodeCode Available	1
Relation Networks for Object Detection	Nov 30, 2017	Objectobject-detection	CodeCode Available	1
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices	Sep 6, 2017	Distributed ComputingObject Recognition	CodeCode Available	1
Multiple Instance Detection Network with Online Instance Classifier Refinement	Apr 1, 2017	Multiple Instance LearningObject	CodeCode Available	1
Evolving Deep Neural Networks	Mar 1, 2017	Deep LearningImage Captioning	CodeCode Available	1
Densely Connected Convolutional Networks	Aug 25, 2016	Breast Tumour ClassificationClassification	CodeCode Available	1
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning	May 25, 2016	Object RecognitionVideo Prediction	CodeCode Available	1
Domain Generalization for Object Recognition with Multi-task Autoencoders	Aug 31, 2015	DenoisingDomain Generalization	CodeCode Available	1
Training Deep Neural Networks on Noisy Labels with Bootstrapping	Dec 20, 2014	Emotion RecognitionObject Recognition	CodeCode Available	1
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet	Nov 4, 2014	Object RecognitionPoint Processes	CodeCode Available	1
Going Deeper with Convolutions	Sep 17, 2014	General ClassificationImage Classification	CodeCode Available	1
ImageNet Large Scale Visual Recognition Challenge	Sep 1, 2014	General Classificationimage-classification	CodeCode Available	1
3D ShapeNets: A Deep Representation for Volumetric Shapes	Jun 22, 2014	3D Point Cloud Classification3D Shape Representation	CodeCode Available	1
Microsoft COCO: Common Objects in Context	May 1, 2014	Instance SegmentationObject	CodeCode Available	1
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks	Dec 21, 2013	General ClassificationImage Classification	CodeCode Available	1
Describing Textures in the Wild	Nov 14, 2013	Material RecognitionObject Recognition	CodeCode Available	1
Improving neural networks by preventing co-adaptation of feature detectors	Jul 3, 2012	Image ClassificationObject Recognition	CodeCode Available	1
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing	Jul 8, 2025	Language ModelingLanguage Modelling	—Unverified	0
Out-of-distribution detection in 3D applications: a review	Jul 1, 2025	Autonomous DrivingNavigate	—Unverified	0
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds	Jun 16, 2025	3D Object RecognitionObject Recognition	CodeCode Available	0
Continual Hyperbolic Learning of Instances and Classes	Jun 12, 2025	Continual LearningObject Recognition	—Unverified	0

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All datasets shape bias CIFAR10-DVS N-Caltech 101 ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 Gesture MECCANO N-CARS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Imagen	shape bias	98.7	—	Unverified
2	Stable Diffusion	shape bias	92.7	—	Unverified
3	Parti	shape bias	91.7	—	Unverified
4	ViT-22B-384	shape bias	86.4	—	Unverified
5	ViT-22B-560	shape bias	83.8	—	Unverified
6	CLIP (ViT-B)	shape bias	79.9	—	Unverified
7	ViT-22B-224	shape bias	78	—	Unverified
8	ResNet-50 (L2 eps 5.0 adv trained)	shape bias	69.5	—	Unverified
9	ResNet-50 (with strong augmentations)	shape bias	62.2	—	Unverified
10	SWSL (ResNeXt-101)	shape bias	49.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.55	—	Unverified
2	SSNN	Accuracy (% )	78.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.62	—	Unverified
2	SSNN	Accuracy (% )	79.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	18.75	—	Unverified
2	yun	Top 5 Accuracy	14.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	DY	Top 5 Accuracy	0.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	AJ2021	Top 5 Accuracy	27.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSNN	Accuracy (% )	94.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Faster-RCNN	mAP	30.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	96	—	Unverified