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 1481–1490 of 2042 papers

Title	Date	Tasks	Status
Analysis and Optimization of Convolutional Neural Network Architectures	Jul 31, 2017	Data AugmentationObject Recognition	CodeCode Available
Fashioning with Networks: Neural Style Transfer to Design Clothes	Jul 31, 2017	Image Segmentationobject-detection	—Unverified
An Effective Training Method For Deep Convolutional Neural Network	Jul 31, 2017	Object Recognition	—Unverified
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs	Jul 25, 2017	object-detectionObject Detection	—Unverified
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach	Jul 24, 2017	feature selectionObject Recognition	—Unverified
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture	Jul 21, 2017	Incremental LearningObject Recognition	—Unverified
Improving Gibbs Sampler Scan Quality with DoGS	Jul 18, 2017	Image SegmentationObject Recognition	—Unverified
Fast Feature Fool: A data independent approach to universal adversarial perturbations	Jul 18, 2017	Object Recognition	CodeCode Available
Robust Visual Tracking via Hierarchical Convolutional Features	Jul 12, 2017	Object RecognitionVisual Tracking	CodeCode Available
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition	Jul 8, 2017	ObjectObject 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