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 1761–1770 of 2042 papers

Title	Date	Tasks	Status
Combinatorial clustering and the beta negative binomial process	Nov 8, 2011	ClusteringImage Segmentation	—Unverified
Combined Approach for Image Segmentation	May 31, 2014	ClusteringDenoising	—Unverified
Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition	Jul 28, 2024	Object Recognition	—Unverified
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification	Nov 20, 2020	Emotion ClassificationGeneral Classification	—Unverified
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images	Aug 1, 2016	Object DetectionObject Recognition	—Unverified
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces	Sep 19, 2020	3D Object RecognitionDescriptive	—Unverified
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision	Sep 14, 2016	General ClassificationImage Retrieval	—Unverified
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics	Nov 1, 2016	Image RetrievalInformation Retrieval	—Unverified
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study	Jul 30, 2021	Object Recognition	—Unverified
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets	Nov 25, 2024	DiversityObject 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