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 741–750 of 2042 papers

Title	Date	Tasks	Status
Grassmannian learning mutual subspace method for image set recognition	Nov 8, 2021	Emotion RecognitionFace Identification	—Unverified
Development of collective behavior in newborn artificial agents	Nov 6, 2021	Deep Reinforcement LearningObject Recognition	—Unverified
Certifiable Artificial Intelligence Through Data Fusion	Nov 3, 2021	Object Recognition	—Unverified
Resampling and super-resolution of hexagonally sampled images using deep learning	Nov 3, 2021	Deep LearningObject Recognition	—Unverified
Investigating Negation in Pre-trained Vision-and-language Models	Nov 1, 2021	NegationObject Recognition	CodeCode Available
Latent Cognizance: What Machine Really Learns	Oct 29, 2021	Object RecognitionOpen Set Learning	—Unverified
HR-RCNN: Hierarchical Relational Reasoning for Object Detection	Oct 26, 2021	Graph AttentionInstance Segmentation	—Unverified
MVT: Multi-view Vision Transformer for 3D Object Recognition	Oct 25, 2021	3D Object RecognitionInductive Bias	CodeCode Available
On Label-Eﬀicient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers	Oct 19, 2021	Active LearningContinual Learning	—Unverified
Knowledge-driven Active Learning	Oct 15, 2021	Active LearningMulti-Label Classification	CodeCode Available

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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