Object Localization

Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.

Source: Fast On-Line Kernel Density Estimation for Active Object Localization

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 171–180 of 617 papers

Title	Date	Tasks	Status
Deep learning architectures for automated image segmentation	Sep 19, 2019	Boundary DetectionDecoder	—Unverified
Deep Joint Task Learning for Generic Object Extraction	Feb 3, 2015	ObjectObject Localization	—Unverified
Geometry Aligned Variational Transformer for Image-conditioned Layout Generation	Sep 2, 2022	Layout DesignLayout Generation	—Unverified
Deep Contextual Attention for Human-Object Interaction Detection	Oct 17, 2019	Human-Object Interaction DetectionObject	—Unverified
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality	Sep 22, 2023	Collaborative InferenceFeature Compression	—Unverified
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization	Oct 4, 2019	DenoisingMultiple Instance Learning	—Unverified
Modelling Lips-State Detection Using CNN for Non-Verbal Communications	Dec 9, 2021	License Plate DetectionLicense Plate Recognition	—Unverified
An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection	Feb 13, 2023	object-detectionObject Detection	—Unverified
D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation	Dec 2, 2022	Domain AdaptationObject	—Unverified
BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairments	Jul 25, 2024	Object Localization	—Unverified

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All datasets REVERIE KITTI Cars Easy KITTI Pedestrians Hard KITTI Pedestrians Moderate KITTI Cars Hard KITTI Cyclists Easy KITTI Cyclists Moderate KITTI Pedestrians Easy GRIT KITTI Cyclists Hard IllusionVQA KITTI Cars Moderate

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	OSMaN	RGSPL	32.99	—	Unverified
2	SUSA	RGSPL	27.31	—	Unverified
3	Shanks	RGSPL	22.85	—	Unverified
4	CVPR22	RGSPL	22.06	—	Unverified
5	damm1	RGSPL	15.96	—	Unverified
6	1637	RGSPL	14.03	—	Unverified
7	init. PREVALENT	RGSPL	13.51	—	Unverified
8	Airbert	RGSPL	13.28	—	Unverified
9	init. OSCAR	RGSPL	10	—	Unverified
10	SIA	RGSPL	9.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VoxelNet	AP	89.35	—	Unverified
2	VoxelNet	AP	89.35	—	Unverified
3	Frustum PointNets	AP	88.7	—	Unverified
4	Frustum PointNets	AP	81.2	—	Unverified
5	VoxelNet	AP	77.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustrum-PointPillars	AP	48.3	—	Unverified
2	Frustum PointNets	AP	47.2	—	Unverified
3	Frustum PointNets	AP	40.23	—	Unverified
4	VoxelNet	AP	38.11	—	Unverified
5	VoxelNet	AP	31.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustrum-PointPillars	AP	52.23	—	Unverified
2	Frustum PointNets	AP	50.22	—	Unverified
3	Frustum PointNets	AP	42.15	—	Unverified
4	VoxelNet	AP	40.74	—	Unverified
5	VoxelNet	AP	33.69	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VoxelNet	AP	77.39	—	Unverified
2	Frustum PointNets	AP	75.33	—	Unverified
3	Frustum PointNets	AP	62.19	—	Unverified
4	VoxelNet	AP	57.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustum PointNets	AP	75.38	—	Unverified
2	Frustum PointNets	AP	71.96	—	Unverified
3	VoxelNet	AP	66.7	—	Unverified
4	VoxelNet	AP	61.22	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustum PointNets	AP	61.96	—	Unverified
2	Frustum PointNets	AP	56.77	—	Unverified
3	VoxelNet	AP	54.76	—	Unverified
4	VoxelNet	AP	48.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustum PointNets	AP	58.09	—	Unverified
2	Frustum PointNets	AP	51.21	—	Unverified
3	VoxelNet	AP	46.13	—	Unverified
4	VoxelNet	AP	39.48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Unified-IOXL	Localization (ablation)	67	—	Unverified
2	GPV-2	Localization (ablation)	53.6	—	Unverified
3	Mask R-CNN	Localization (ablation)	44.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustum PointNets	AP	54.68	—	Unverified
2	VoxelNe	AP	50.55	—	Unverified
3	Frustum PointNets	AP	50.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GPT4-Vision 4-shot+CoT	Accuracy	49.7	—	Unverified
2	Gemini-Pro 4-shot+CoT	Accuracy	33.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustum PointNets	AP	84	—	Unverified
2	VoxelNet	AP	79.26	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Frustrum-PointPillars	AP	60.98	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hausdorff Loss	Precision	88.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ours	CorLoc	41.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ours	CorLoc	47.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hausdorff Loss	F-Score	88.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hausdorff Loss	Recall	89.2	—	Unverified