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 1451–1460 of 2042 papers

Title	Date	Tasks	Status
Memory Aware Synapses: Learning what (not) to forget	Nov 27, 2017	Lifelong learningObject Recognition	CodeCode Available
Context Augmentation for Convolutional Neural Networks	Nov 22, 2017	General Classificationimage-classification	—Unverified
Glitch Classification and Clustering for LIGO with Deep Transfer Learning	Nov 20, 2017	ClassificationClustering	—Unverified
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning	Nov 19, 2017	Object Recognition	—Unverified
ADVISE: Symbolism and External Knowledge for Decoding Advertisements	Nov 17, 2017	ClusteringImage Captioning	—Unverified
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment	Nov 16, 2017	Domain AdaptationObject Recognition	CodeCode Available
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes	Nov 13, 2017	3D Object RecognitionDecision Making	CodeCode Available
Latent Constrained Correlation Filter	Nov 11, 2017	Object RecognitionObject Tracking	—Unverified
Analysis of Dropout in Online Learning	Nov 9, 2017	Object Recognitionspeech-recognition	—Unverified
Interpreting Convolutional Neural Networks Through Compression	Nov 7, 2017	Object Recognition	—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