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 621–630 of 2042 papers

Title	Date	Tasks	Status	Hype
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks	Nov 24, 2021	Anomaly DetectionColorization	—Unverified	0
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation	Nov 24, 2021	Event-based Object SegmentationKnowledge Distillation	CodeCode Available	1
Object Recognition by a Minimally Pre-Trained System in the Process of Environment Exploration	Nov 23, 2021	ObjectObject Recognition	—Unverified	0
Inducing Functions through Reinforcement Learning without Task Specification	Nov 23, 2021	Decision Makingimage-classification	—Unverified	0
Adversarial Examples on Segmentation Models Can be Easy to Transfer	Nov 22, 2021	Adversarial RobustnessAttribute	—Unverified	0
The Neural Correlates of Image Texture in the Human Vision Using Magnetoencephalography	Nov 16, 2021	Object Recognition	—Unverified	0
ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching	Nov 16, 2021	ObjectObject Recognition	—Unverified	0
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge	Nov 15, 2021	Instance SegmentationObject Recognition	—Unverified	0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping	Nov 11, 2021	3D Object Recognition6D Pose Estimation	—Unverified	0
Unsupervised Spiking Instance Segmentation on Event Data using STDP	Nov 9, 2021	Event-based visionFace Detection	—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