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 1631–1640 of 2042 papers

Title	Date	Tasks	Status
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics	Aug 9, 2016	Object RecognitionSpecificity	—Unverified
Adapting Deep Network Features to Capture Psychological Representations	Aug 6, 2016	Object RecognitionScene Understanding	CodeCode Available
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations	Aug 4, 2016	Missing LabelsMulti-Label Learning	—Unverified
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images	Aug 1, 2016	Object DetectionObject Recognition	—Unverified
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis	Aug 1, 2016	Action ClassificationObject Recognition	—Unverified
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition	Jul 29, 2016	Depiction Invariant Object RecognitionObject	CodeCode Available
Feature Descriptors for Tracking by Detection: a Benchmark	Jul 21, 2016	3D ReconstructionObject Recognition	—Unverified
Improved Deep Learning of Object Category using Pose Information	Jul 20, 2016	Deep LearningObject	—Unverified
Learning to Recognize Objects by Retaining other Factors of Variation	Jul 20, 2016	ObjectObject Recognition	—Unverified
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods	Jul 19, 2016	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