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 471–480 of 2042 papers

Title	Date	Tasks	Status	Hype
Object Recognition in Different Lighting Conditions at Various Angles by Deep Learning Method	Oct 18, 2022	Autonomous DrivingObject	—Unverified	0
Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models	Oct 16, 2022	Object Recognition	—Unverified	0
Variant Parallelism: Lightweight Deep Convolutional Models for Distributed Inference on IoT Devices	Oct 15, 2022	2kObject Recognition	—Unverified	0
Hand Gestures Recognition in Videos Taken with Lensless Camera	Oct 15, 2022	Cloud ComputingImage Reconstruction	—Unverified	0
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs	Oct 12, 2022	Object Recognition	CodeCode Available	1
Learning by Asking Questions for Knowledge-based Novel Object Recognition	Oct 12, 2022	ObjectObject Recognition	—Unverified	0
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition	Oct 11, 2022	image-classificationImage Classification	CodeCode Available	2
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping	Oct 7, 2022	ObjectObject Recognition	—Unverified	0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models	Oct 3, 2022	3D Object RecognitionFine-Grained Image Classification	CodeCode Available	0
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition	Oct 3, 2022	3D Object RecognitionLifelong learning	—Unverified	0

Show:10 25 50

← PrevPage 48 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