SOTAVerified

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

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Showing 351–400 of 617 papers

TitleStatusHype
Improving Few-shot Learning with Weakly-supervised Object Localization—0
Improving Weakly-Supervised Object Localization By Micro-Annotation—0
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label—0
Information Entropy Based Feature Pooling for Convolutional Neural Networks—0
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization—0
Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation—0
I see what you hear: a vision-inspired method to localize words—0
Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks—0
Iterative Spectral Clustering for Unsupervised Object Localization—0
Joint ANN-SNN Co-training for Object Localization and Image Segmentation—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Towards Accurate Localization by Instance Search—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—0
Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning—0
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization—0
Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking—0
Latent Constrained Correlation Filters for Object Localization—0
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization—0
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions—0
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization—0
Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models—0
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation—0
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization—0
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images—0
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Learning task-agnostic representation via toddler-inspired learning—0
Learning to Detect Instance-level Salient Objects Using Complementary Image Labels—0
Learning to Grasp Without Seeing—0
Learning to search for and detect objects in foveal images using deep learning—0
Leveraging Activations for Superpixel Explanations—0
Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videos—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
LIT: Light-field Inference of Transparency for Refractive Object Localization—0
Localization: A Missing Link in the Pipeline of Object Matching and Registration—0
Towards Omnidirectional Reasoning with 360-R1: A Dataset, Benchmark, and GRPO-based Method—0
Adaptive Label Smoothing—0
Locating 3D Object Proposals: A Depth-Based Online Approach—0
Location-free Human Pose Estimation—0
Towards Two-Stream Foveation-based Active Vision Learning—0
Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging—0
LUDVIG: Learning-free Uplifting of 2D Visual features to Gaussian Splatting scenes—0
Adapting Mask-RCNN for Automatic Nucleus Segmentation—0
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval—0
Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations—0
Maximum Cohesive Grid of Superpixels for Fast Object Localization—0
Max-Margin Structured Output Regression for Spatio-Temporal Action Localization—0
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OSMaNRGSPL32.99—Unverified
2SUSARGSPL27.31—Unverified
3ShanksRGSPL22.85—Unverified
4CVPR22RGSPL22.06—Unverified
5damm1RGSPL15.96—Unverified
61637RGSPL14.03—Unverified
7init. PREVALENTRGSPL13.51—Unverified
8AirbertRGSPL13.28—Unverified
9init. OSCARRGSPL10—Unverified
10SIARGSPL9.2—Unverified
#ModelMetricClaimedVerifiedStatus
1VoxelNetAP89.35—Unverified
2VoxelNetAP89.35—Unverified
3Frustum PointNetsAP88.7—Unverified
4Frustum PointNetsAP81.2—Unverified
5VoxelNetAP77.47—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustrum-PointPillarsAP48.3—Unverified
2Frustum PointNetsAP47.2—Unverified
3Frustum PointNetsAP40.23—Unverified
4VoxelNetAP38.11—Unverified
5VoxelNetAP31.51—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustrum-PointPillarsAP52.23—Unverified
2Frustum PointNetsAP50.22—Unverified
3Frustum PointNetsAP42.15—Unverified
4VoxelNetAP40.74—Unverified
5VoxelNetAP33.69—Unverified
#ModelMetricClaimedVerifiedStatus
1VoxelNetAP77.39—Unverified
2Frustum PointNetsAP75.33—Unverified
3Frustum PointNetsAP62.19—Unverified
4VoxelNetAP57.73—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustum PointNetsAP75.38—Unverified
2Frustum PointNetsAP71.96—Unverified
3VoxelNetAP66.7—Unverified
4VoxelNetAP61.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustum PointNetsAP61.96—Unverified
2Frustum PointNetsAP56.77—Unverified
3VoxelNetAP54.76—Unverified
4VoxelNetAP48.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustum PointNetsAP58.09—Unverified
2Frustum PointNetsAP51.21—Unverified
3VoxelNetAP46.13—Unverified
4VoxelNetAP39.48—Unverified
#ModelMetricClaimedVerifiedStatus
1Unified-IOXLLocalization (ablation)67—Unverified
2GPV-2Localization (ablation)53.6—Unverified
3Mask R-CNNLocalization (ablation)44.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustum PointNetsAP54.68—Unverified
2VoxelNeAP50.55—Unverified
3Frustum PointNetsAP50.39—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT4-Vision 4-shot+CoTAccuracy49.7—Unverified
2Gemini-Pro 4-shot+CoTAccuracy33.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustum PointNetsAP84—Unverified
2VoxelNetAP79.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Frustrum-PointPillarsAP60.98—Unverified
#ModelMetricClaimedVerifiedStatus
1Hausdorff LossPrecision88.1—Unverified
#ModelMetricClaimedVerifiedStatus
1oursCorLoc41.2—Unverified
#ModelMetricClaimedVerifiedStatus
1oursCorLoc47.45—Unverified
#ModelMetricClaimedVerifiedStatus
1Hausdorff LossF-Score88.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Hausdorff LossRecall89.2—Unverified