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 521–530 of 2042 papers

Title	Date	Tasks	Status	Hype
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images	Jun 30, 2022	Federated LearningObject Recognition	—Unverified	0
Deep Optical Coding Design in Computational Imaging	Jun 27, 2022	3D Object RecognitionDecoder	—Unverified	0
Complementary datasets to COCO for object detection	Jun 23, 2022	Objectobject-detection	CodeCode Available	0
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering	Jun 21, 2022	3D Object RecognitionObject Recognition	—Unverified	0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements	Jun 21, 2022	Face DetectionObject Recognition	—Unverified	0
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection	Jun 20, 2022	Image CaptioningImage Generation	—Unverified	0
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks	Jun 16, 2022	BenchmarkingDynamic neural networks	CodeCode Available	0
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency	Jun 16, 2022	Domain AdaptationObject Recognition	CodeCode Available	1
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises	Jun 15, 2022	Adversarial RobustnessFoveation	CodeCode Available	0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models	Jun 14, 2022	AttributeBIG-bench Machine Learning	—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