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 121–130 of 2042 papers

Title	Date	Tasks	Status	Hype
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning	Oct 25, 2021	Arithmetic ReasoningMathematical Question Answering	CodeCode Available	1
Explainability-Aware One Point Attack for Point Cloud Neural Networks	Oct 8, 2021	3D Object RecognitionAdversarial Robustness	CodeCode Available	1
Voxel Transformer for 3D Object Detection	Sep 6, 2021	3D Object DetectionComputational Efficiency	CodeCode Available	1
Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor	Aug 12, 2021	Object RecognitionSegmentation	CodeCode Available	1
On the Challenges of Open World Recognitionunder Shifting Visual Domains	Jul 9, 2021	Domain GeneralizationObject Recognition	CodeCode Available	1
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering	Jul 6, 2021	Active LearningObject Recognition	CodeCode Available	1
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks	Jul 4, 2021	Deep LearningObject Recognition	CodeCode Available	1
Wasserstein Barycenter for Multi-Source Domain Adaptation	Jun 19, 2021	Domain AdaptationFace Recognition	CodeCode Available	1
Deep Subdomain Adaptation Network for Image Classification	Jun 17, 2021	ClassificationDomain Adaptation	CodeCode Available	1
Self-Supervised Learning with Kernel Dependence Maximization	Jun 15, 2021	Depth EstimationLinear evaluation	CodeCode Available	1

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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