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 1371–1380 of 2042 papers

Title	Date	Tasks	Status
A Classification approach towards Unsupervised Learning of Visual Representations	Jun 1, 2018	ClassificationGeneral Classification	CodeCode Available
Multi-View Harmonized Bilinear Network for 3D Object Recognition	Jun 1, 2018	3D Object RecognitionObject	—Unverified
OLÃ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep Learning	Jun 1, 2018	General Classificationimage-classification	CodeCode Available
Geometry Aware Constrained Optimization Techniques for Deep Learning	Jun 1, 2018	Deep LearningMetric Learning	—Unverified
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation	Jun 1, 2018	Domain AdaptationGenerative Adversarial Network	—Unverified
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash	May 30, 2018	Image EnhancementObject Recognition	—Unverified
Multi-level 3D CNN for Learning Multi-scale Spatial Features	May 30, 2018	3D Object RecognitionObject	CodeCode Available
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks	May 28, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization	May 28, 2018	Active LearningContinuous Object Recognition	CodeCode Available
Deep Watershed Detector for Music Object Recognition	May 26, 2018	Information RetrievalMusic Information Retrieval	—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