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 1291–1300 of 2042 papers

Title	Date	Tasks	Status
Textured As-Is BIM via GIS-informed Point Cloud Segmentation	Nov 28, 2024	Object RecognitionPoint Cloud Segmentation	—Unverified
Texture image analysis and texture classification methods - A review	Apr 13, 2019	ClassificationDefect Detection	—Unverified
Textureless Object Recognition: An Edge-based Approach	Mar 10, 2024	ObjectObject Recognition	—Unverified
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences	Jun 14, 2024	Depth EstimationImage Segmentation	—Unverified
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition	May 17, 2015	Action RecognitionMulti-class Classification	—Unverified
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks	Sep 30, 2021	ObjectObject Recognition	—Unverified
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks	Feb 6, 2020	Object RecognitionTransfer Learning	—Unverified
The Distribution Family of Similarity Distances	Dec 1, 2007	Object RecognitionScene Recognition	—Unverified
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs	Jan 26, 2021	Image ManipulationObject Recognition	—Unverified
The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance	Oct 31, 2018	Object 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