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 1851–1860 of 2042 papers

Title	Date	Tasks	Status
Causal importance of orientation selectivity for generalization in image recognition	May 1, 2019	image-classificationImage Classification	CodeCode Available
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts	Nov 18, 2024	BenchmarkingMultimodal Large Language Model	CodeCode Available
UAVid: A Semantic Segmentation Dataset for UAV Imagery	Oct 24, 2018	4kAutonomous Driving	CodeCode Available
Incremental Robot Learning of New Objects with Fixed Update Time	May 17, 2016	Active LearningGeneral Classification	CodeCode Available
Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives	Oct 9, 2019	ClusteringObject Recognition	CodeCode Available
Directional Regularized Tensor Modeling for Video Rain Streaks Removal	Feb 19, 2019	Object RecognitionRain Removal	CodeCode Available
Infinite Feature Selection	Dec 1, 2015	Classificationfeature selection	CodeCode Available
Semantically Meaningful View Selection	Jul 26, 2018	ClusteringObject	CodeCode Available
Quaternion Capsule Networks	Jul 8, 2020	BenchmarkingObject Recognition	CodeCode Available
Different Spectral Representations in Optimized Artificial Neural Networks and Brains	Aug 22, 2022	Adversarial AttackAdversarial Robustness	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