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 1301–1310 of 2042 papers

Title	Date	Tasks	Status	Hype
Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network	Oct 3, 2018	Image RestorationObject	—Unverified	0
Knowledge-guided Semantic Computing Network	Sep 29, 2018	Adversarial RobustnessObject Recognition	—Unverified	0
Deep learning systems as complex networks	Sep 28, 2018	Deep LearningNatural Language Understanding	—Unverified	0
DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING	Sep 27, 2018	ClassificationObject Recognition	—Unverified	0
Semantic Topic Analysis of Traffic Camera Images	Sep 27, 2018	Data CompressionObject Recognition	—Unverified	0
Hypergraph Neural Networks	Sep 25, 2018	Object RecognitionRepresentation Learning	CodeCode Available	2
Semantic and structural image segmentation for prosthetic vision	Sep 25, 2018	Image SegmentationObject	—Unverified	0
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions	Sep 22, 2018	ObjectObject Recognition	—Unverified	0
Periocular Recognition Using CNN Features Off-the-Shelf	Sep 17, 2018	Object Recognition	—Unverified	0
Transparency and Explanation in Deep Reinforcement Learning Neural Networks	Sep 17, 2018	Atari GamesDeep Reinforcement Learning	—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