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 731–740 of 2042 papers

Title	Date	Tasks	Status	Hype
ORCEA: Object Recognition by Continuous Evidence Assimilation	May 11, 2021	ObjectObject Recognition	—Unverified	0
BIM Hyperreality: Data Synthesis Using BIM and Hyperrealistic Rendering for Deep Learning	May 10, 2021	Generative Adversarial NetworkObject Recognition	—Unverified	0
Modelling of LIDAR sensor disturbances by solid airborne particles	May 10, 2021	object-detectionObject Detection	—Unverified	0
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks	May 5, 2021	Explainable artificial intelligenceImage Classification	CodeCode Available	1
Canonical Saliency Maps: Decoding Deep Face Models	May 4, 2021	Face ModelObject Recognition	CodeCode Available	0
Learning data association without data association: An EM approach to neural assignment prediction	May 2, 2021	Multi-Object TrackingObject	—Unverified	0
IPatch: A Remote Adversarial Patch	Apr 30, 2021	Autonomous VehiclesImage Segmentation	—Unverified	0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects	Apr 26, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available	0
RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition	Apr 24, 2021	Image CaptioningObject Recognition	CodeCode Available	1
Recurrent Feedback Improves Recognition of Partially Occluded Objects	Apr 21, 2021	ObjectObject 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