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 631–640 of 2042 papers

Title	Date	Tasks	Status
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving	Feb 27, 2023	3D Object DetectionAutonomous Driving	—Unverified
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation	Jun 1, 2018	Domain AdaptationGenerative Adversarial Network	—Unverified
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment	Apr 16, 2025	Language ModelingLanguage Modelling	—Unverified
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation	Feb 2, 2023	Object Recognition	—Unverified
A neuromorphic approach to image processing and machine vision	Aug 7, 2022	Image SegmentationObject Recognition	—Unverified
Cascade Region Proposal and Global Context for Deep Object Detection	Oct 30, 2017	Objectobject-detection	—Unverified
Dynamic reshaping of functional brain networks during visual object recognition	Jul 31, 2018	EEGElectroencephalogram (EEG)	—Unverified
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields	Oct 13, 2017	DescriptiveDynamic Texture Recognition	—Unverified
Advancing Egocentric Video Question Answering with Multimodal Large Language Models	Apr 6, 2025	Object RecognitionQuestion Answering	—Unverified
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning	Dec 6, 2019	ObjectObject Recognition	—Unverified

Show:10 25 50

← PrevPage 64 of 205Next →

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