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 2011–2020 of 2042 papers

Title	Date	Tasks	Status
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition	Jun 18, 2014	General Classificationimage-classification	CodeCode Available
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream	Jan 1, 2021	Developmental LearningObject Recognition	CodeCode Available
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models	May 11, 2019	ObjectObject Recognition	CodeCode Available
DeepID3: Face Recognition with Very Deep Neural Networks	Feb 3, 2015	Face IdentificationFace Recognition	CodeCode Available
Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition	Jun 4, 2019	3D Object RecognitionClustering	CodeCode Available
Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images	Apr 15, 2020	Object RecognitionSpectral Reconstruction	CodeCode Available
Domain Generalization via Model-Agnostic Learning of Semantic Features	Oct 29, 2019	Domain GeneralizationGeneral Knowledge	CodeCode Available
Deep Feature Collaboration for Challenging 3D Finger Knuckle Identification	Oct 9, 2020	Object Recognition	CodeCode Available
Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection	Aug 21, 2020	Edge Detectionobject-detection	CodeCode Available
The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks	May 20, 2022	ObjectObject 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