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 1191–1200 of 2042 papers

Title	Date	Tasks	Status
Generic decoding of seen and imagined objects using hierarchical visual features	Oct 22, 2015	DecoderInformation Retrieval	—Unverified
Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks	Jan 15, 2024	Autonomous DrivingAutonomous Vehicles	—Unverified
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing	Jul 8, 2025	Language ModelingLanguage Modelling	—Unverified
Geometry Aware Constrained Optimization Techniques for Deep Learning	Jun 1, 2018	Deep LearningMetric Learning	—Unverified
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning	Apr 6, 2024	Computational EfficiencyModel Optimization	—Unverified
Glitch Classification and Clustering for LIGO with Deep Transfer Learning	Nov 20, 2017	ClassificationClustering	—Unverified
Global Deconvolutional Networks for Semantic Segmentation	Feb 12, 2016	Autonomous Drivingimage-classification	—Unverified
Going Deeper into Action Recognition: A Survey	May 16, 2016	Action AnalysisAction Recognition	—Unverified
Gradient-based Laplacian Feature Selection	Apr 10, 2014	feature selectionObject Recognition	—Unverified
Gradients of Counterfactuals	Nov 8, 2016	counterfactualFeature Importance	—Unverified

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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