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 1381–1390 of 2042 papers

Title	Date	Tasks	Status	Hype
Performance Evaluation of 3D Correspondence Grouping Algorithms	Apr 6, 2018	3D Object RecognitionObject Recognition	—Unverified	0
Hierarchical Novelty Detection for Visual Object Recognition	Apr 2, 2018	Generalized Zero-Shot LearningNovelty Detection	—Unverified	0
Learning Beyond Human Expertise with Generative Models for Dental Restorations	Mar 30, 2018	Object Recognition	—Unverified	0
What deep learning can tell us about higher cognitive functions like mindreading?	Mar 28, 2018	Object Recognition	—Unverified	0
Deep Learning Object Detection Methods for Ecological Camera Trap Data	Mar 28, 2018	Deep LearningObject	—Unverified	0
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects	Mar 27, 2018	General ClassificationObject	CodeCode Available	1
Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision	Mar 26, 2018	Object Recognition	—Unverified	0
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach	Mar 26, 2018	Face RecognitionGeneral Classification	—Unverified	0
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery	Mar 26, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment	Mar 23, 2018	Object Recognition	—Unverified	0

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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