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 1111–1120 of 2042 papers

Title	Date	Tasks	Status
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs	Sep 13, 2019	AnatomyBIG-bench Machine Learning	CodeCode Available
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks	Sep 13, 2019	NutritionObject Recognition	CodeCode Available
Recurrent Connectivity Aids Recognition of Partly Occluded Objects	Sep 12, 2019	ObjectObject Recognition	—Unverified
Foveated Downsampling Techniques	Sep 11, 2019	FoveationObject Recognition	—Unverified
The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units	Sep 11, 2019	Object Recognition	—Unverified
Significance of feedforward architectural differences between the ventral visual stream and DenseNet	Sep 11, 2019	Object Recognition	—Unverified
How well do deep neural networks trained on object recognition characterize the mouse visual system?	Sep 11, 2019	Object Recognition	—Unverified
Dual-attention Focused Module for Weakly Supervised Object Localization	Sep 11, 2019	ObjectObject Localization	—Unverified
DaTscan SPECT Image Classification for Parkinson's Disease	Sep 9, 2019	ClassificationDiagnostic	—Unverified
TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion	Sep 9, 2019	General ClassificationObject	—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