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 1231–1240 of 2042 papers

Title	Date	Tasks	Status
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design	Mar 18, 2019	object-detectionObject Detection	—Unverified
Direct Object Recognition Without Line-of-Sight Using Optical Coherence	Mar 18, 2019	ObjectObject Recognition	—Unverified
Robust Shape Regularity Criteria for Superpixel Evaluation	Mar 17, 2019	Object Recognition	—Unverified
Spatiotemporal Feature Learning for Event-Based Vision	Mar 16, 2019	Event-based visionObject Recognition	—Unverified
Domain Generalization by Solving Jigsaw Puzzles	Mar 16, 2019	Domain GeneralizationImage Classification	CodeCode Available
Visual recognition in the wild by sampling deep similarity functions	Mar 15, 2019	Object Recognition	—Unverified
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis	Mar 13, 2019	Edge DetectionFace Detection	—Unverified
Image Privacy Prediction Using Deep Neural Networks	Mar 8, 2019	Object RecognitionPrediction	CodeCode Available
Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss	Mar 7, 2019	Domain AdaptationGeneral Classification	CodeCode Available
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing	Mar 7, 2019	Object RecognitionQuestion Answering	—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