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 1091–1100 of 2042 papers

Title	Date	Tasks	Status
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching	May 22, 2018	Domain AdaptationGraph Matching	—Unverified
Unsupervised Domain Adaptation using Graph Transduction Games	May 6, 2019	Domain AdaptationObject Recognition	—Unverified
Unsupervised feature learning by augmenting single images	Dec 18, 2013	Data AugmentationObject Recognition	—Unverified
Unsupervised Feature Learning by Deep Sparse Coding	Dec 20, 2013	Object Recognition	—Unverified
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation	Sep 23, 2020	Object RecognitionRepresentation Learning	—Unverified
Unsupervised Feature Learning with C-SVDDNet	Dec 23, 2014	Image ClassificationObject Recognition	—Unverified
Unsupervised Foveal Vision Neural Networks with Top-Down Attention	Oct 18, 2020	ObjectObject Recognition	—Unverified
Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos	Dec 1, 2015	ObjectObject Recognition	—Unverified
Unsupervised Learning of Invariant Representations in Hierarchical Architectures	Nov 17, 2013	Object Recognitionspeech-recognition	—Unverified
Unsupervised Learning using Pretrained CNN and Associative Memory Bank	May 2, 2018	Few-Shot Image ClassificationFine-Grained Image Classification	—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