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 1411–1420 of 2042 papers

Title	Date	Tasks	Status
Triplet-Center Loss for Multi-View 3D Object Retrieval	Mar 16, 2018	3D Object Recognition3D Object Retrieval	CodeCode Available
Exponential Discriminative Metric Embedding in Deep Learning	Mar 7, 2018	Deep LearningFace Recognition	—Unverified
A Non-Technical Survey on Deep Convolutional Neural Network Architectures	Mar 6, 2018	image-classificationImage Classification	—Unverified
Categorical Mixture Models on VGGNet activations	Mar 6, 2018	Object Recognition	—Unverified
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition	Mar 6, 2018	ObjectObject Recognition	—Unverified
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning	Feb 26, 2018	ClusteringGeneral Classification	—Unverified
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification	Feb 21, 2018	ClassificationGeneral Classification	CodeCode Available
Do deep nets really need weight decay and dropout?	Feb 20, 2018	Data AugmentationObject Recognition	CodeCode Available
Co-occurrence matrix analysis-based semi-supervised training for object detection	Feb 20, 2018	Objectobject-detection	—Unverified
Scenarios: A New Representation for Complex Scene Understanding	Feb 16, 2018	Image RetrievalObject 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