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 1641–1650 of 2042 papers

Title	Date	Tasks	Status
Towards Bayesian Deep Learning: A Framework and Some Existing Methods	Aug 24, 2016	Deep LearningObject Recognition	—Unverified
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition	Aug 11, 2016	ObjectObject Recognition	CodeCode Available
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics	Aug 9, 2016	Object RecognitionSpecificity	—Unverified
Adapting Deep Network Features to Capture Psychological Representations	Aug 6, 2016	Object RecognitionScene Understanding	CodeCode Available
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations	Aug 4, 2016	Missing LabelsMulti-Label Learning	—Unverified
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images	Aug 1, 2016	Object DetectionObject Recognition	—Unverified
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis	Aug 1, 2016	Action ClassificationObject Recognition	—Unverified
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition	Jul 29, 2016	Depiction Invariant Object RecognitionObject	CodeCode Available
Feature Descriptors for Tracking by Detection: a Benchmark	Jul 21, 2016	3D ReconstructionObject Recognition	—Unverified
Learning to Recognize Objects by Retaining other Factors of Variation	Jul 20, 2016	ObjectObject Recognition	—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