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 281–290 of 2042 papers

Title	Date	Tasks	Status	Hype
Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks	Jan 15, 2024	Autonomous DrivingAutonomous Vehicles	—Unverified	0
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras	Jan 14, 2024	Camera CalibrationObject Recognition	—Unverified	0
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery	Jan 12, 2024	Object RecognitionRoad Segmentation	CodeCode Available	2
Meta-forests: Domain generalization on random forests with meta-learning	Jan 9, 2024	Domain GeneralizationMeta-Learning	—Unverified	0
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition	Jan 3, 2024	DescriptiveLanguage Modeling	—Unverified	0
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network	Jan 2, 2024	Data AugmentationEvent data classification	—Unverified	0
Layerwise complexity-matched learning yields an improved model of cortical area V2	Dec 18, 2023	Object Recognition	—Unverified	0
Object Recognition from Scientific Document based on Compartment Refinement Framework	Dec 14, 2023	ArticlesDocument Layout Analysis	—Unverified	0
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data	Dec 14, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
Representational constraints underlying similarity between task-optimized neural systems	Dec 13, 2023	Object Recognition	—Unverified	0

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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