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 1941–1950 of 2042 papers

Title	Date	Tasks	Status
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning	Jun 7, 2019	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available
BViT: Broad Attention based Vision Transformer	Feb 13, 2022	image-classificationImage Classification	CodeCode Available
Deep supervised learning for hyperspectral data classification through convolutional neural networks	Jul 26, 2015	ClassificationFew-Shot Image Classification	CodeCode Available
Learning compact binary descriptors with unsupervised deep neural networks	Jun 1, 2016	Image RetrievalObject	CodeCode Available
Ensemble learning in CNN augmented with fully connected subnetworks	Mar 19, 2020	Ensemble LearningImage Classification	CodeCode Available
OLÃ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep Learning	Jun 1, 2018	General Classificationimage-classification	CodeCode Available
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning	Dec 5, 2017	General Classificationimage-classification	CodeCode Available
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition	May 24, 2024	Object Recognition	CodeCode Available
DeepSat - A Learning framework for Satellite Imagery	Sep 11, 2015	ClassificationDenoising	CodeCode Available
Triangle-Net: Towards Robustness in Point Cloud Learning	Feb 27, 2020	3D Classification3D Object Recognition	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