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 51–60 of 617 papers

Title	Date	Tasks	Status	Hype
Agent Journey Beyond RGB: Unveiling Hybrid Semantic-Spatial Environmental Representations for Vision-and-Language Navigation	Dec 9, 2024	Object LocalizationVision and Language Navigation	CodeCode Available	1
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection	Dec 4, 2023	3D Object DetectionDecoder	CodeCode Available	1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search	May 19, 2022	Decision MakingImage Captioning	CodeCode Available	1
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation	Dec 21, 2023	Edge DetectionFeature Engineering	CodeCode Available	1
CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching	Mar 23, 2023	Described Object Detectionobject-detection	CodeCode Available	1
Efficient Object Localization Using Convolutional Networks	Nov 16, 2014	ObjectObject Localization	CodeCode Available	1
Eigen-CAM: Class Activation Map using Principal Components	Aug 1, 2020	Object LocalizationWeakly-Supervised Object Localization	CodeCode Available	1
Evaluating Weakly Supervised Object Localization Methods Right	Jan 21, 2020	Few-Shot LearningModel Selection	CodeCode Available	1
Boosting Segment Anything Model Towards Open-Vocabulary Learning	Dec 6, 2023	modelObject	CodeCode Available	1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling	Nov 23, 2021	Image CaptioningImage Description	CodeCode Available	1

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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