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 1811–1820 of 2042 papers

Title	Date	Tasks	Status
Food Image Recognition by Using Convolutional Neural Networks (CNNs)	Dec 3, 2016	Data AugmentationGeneral Classification	CodeCode Available
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving	Jul 11, 2021	Autonomous DrivingDecision Making	CodeCode Available
Fit to Measure: Reasoning about Sizes for Robust Object Recognition	Oct 27, 2020	BIG-bench Machine LearningObject	CodeCode Available
Multi-stage Deep Classifier Cascades for Open World Recognition	Aug 26, 2019	Object Recognition	CodeCode Available
Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss	Mar 7, 2019	Domain AdaptationGeneral Classification	CodeCode Available
Image Privacy Prediction Using Deep Neural Networks	Mar 8, 2019	Object RecognitionPrediction	CodeCode Available
Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction	Sep 2, 2020	Content-Based Image RetrievalImage Reconstruction	CodeCode Available
Image Style Transfer Using Convolutional Neural Networks	Jun 1, 2016	Image GenerationObject Recognition	CodeCode Available
Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using Efficient Low-Dimensional Signals	May 2, 2024	ObjectObject Recognition	CodeCode Available
SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving	Jul 18, 2024	Autonomous DrivingImage Generation	CodeCode Available

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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