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 1791–1800 of 2042 papers

Title	Date	Tasks	Status
Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions	Sep 1, 2015	Domain AdaptationImage Captioning	—Unverified
Partitioning Large Scale Deep Belief Networks Using Dropout	Aug 28, 2015	BIG-bench Machine LearningGPU	—Unverified
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition	Aug 17, 2015	Object Recognition	—Unverified
Places205-VGGNet Models for Scene Recognition	Aug 7, 2015	Computational EfficiencyGPU	CodeCode Available
Sublinear Partition Estimation	Aug 7, 2015	Language ModelingLanguage Modelling	CodeCode Available
HFirst: A Temporal Approach to Object Recognition	Aug 5, 2015	ObjectObject Recognition	—Unverified
Deep supervised learning for hyperspectral data classification through convolutional neural networks	Jul 26, 2015	ClassificationFew-Shot Image Classification	CodeCode Available
Thinning Algorithm Using Hypergraph Based Morphological Operators	Jul 25, 2015	Object Recognition	—Unverified
Multimodal Deep Learning for Robust RGB-D Object Recognition	Jul 24, 2015	Data AugmentationDeep Learning	CodeCode Available
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition	Jul 23, 2015	Object RecognitionTranslation	—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