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 1911–1920 of 2042 papers

Title	Date	Tasks	Status
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning	Jul 18, 2019	DecoderGeneralized Zero-Shot Learning	—Unverified
Discriminative Ferns Ensemble for Hand Pose Recognition	Jun 1, 2014	ClassificationGeneral Classification	—Unverified
Discriminatively Trained Sparse Code Gradients for Contour Detection	Dec 1, 2012	Contour DetectionDictionary Learning	—Unverified
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition	Jun 1, 2016	Image SegmentationObject Recognition	—Unverified
Disentangled Deep Autoencoding Regularization for Robust Image Classification	Feb 27, 2019	ClassificationGeneral Classification	—Unverified
Disentangling Properties of Contrastive Methods	Sep 29, 2021	DisentanglementObject Recognition	—Unverified
Distributed Coding of Multiview Sparse Sources with Joint Recovery	Jul 18, 2016	compressed sensingObject Recognition	—Unverified
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN	May 3, 2023	Instance SegmentationObject Recognition	—Unverified
Diversity in Object Proposals	Mar 14, 2016	DiversityObject	—Unverified
DNN Quantization with Attention	Mar 24, 2021	Object RecognitionQuantization	—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