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 681–690 of 2042 papers

Title	Date	Tasks	Status
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors	Nov 24, 2014	Fine-Grained Image ClassificationGeneral Classification	—Unverified
End-to-End Auditory Object Recognition via Inception Nucleus	May 25, 2020	ClassificationGeneral Classification	—Unverified
End-to-end Binary Representation Learning via Direct Binary Embedding	Mar 15, 2017	Image RetrievalObject Recognition	—Unverified
Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images	Jul 22, 2019	3D Object RecognitionAutonomous Driving	—Unverified
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning	Dec 6, 2019	ObjectObject Recognition	—Unverified
End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions	Aug 18, 2023	AllObject Recognition	—Unverified
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment	Mar 23, 2018	Object Recognition	—Unverified
Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs	Aug 1, 2018	GPUObject Recognition	—Unverified
Energy Efficient Hadamard Neural Networks	May 14, 2018	Deep LearningGeneral Classification	—Unverified
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN	May 3, 2023	Instance SegmentationObject 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