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 401–450 of 617 papers

TitleStatusHype
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization—0
Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation—0
Weakly Supervised Object Localization and Detection: A Survey—0
Fine-Grained Attention for Weakly Supervised Object Localization—0
Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient ImagesCode0
DAP: Detection-Aware Pre-training with Weak SupervisionCode0
Few-shot Weakly-Supervised Object Detection via Directional Statistics—0
Object Localization Through a Single Multiple-Model Convolutional Neural Network with a Specific Training Approach—0
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
Weakly-supervised multi-class object localization using only object counts as labels—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
Learning task-agnostic representation via toddler-inspired learning—0
Personal Fixations-Based Object Segmentation with Object Localization and Boundary PreservationCode0
Focal and Efficient IOU Loss for Accurate Bounding Box RegressionCode0
Foreground Activation Maps for Weakly Supervised Object Localization—0
One Size Doesn't Fit All: Adaptive Label Smoothing—0
GridMix: Strong regularization through local context mapping—0
One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks—0
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection—0
MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos—0
Video Anomaly Detection by Estimating Likelihood of RepresentationsCode0
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization—0
Hierarchical Complementary Learning for Weakly Supervised Object Localization—0
BirdSLAM: Monocular Multibody SLAM in Bird's-Eye View—0
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels—0
Weakly-supervised Salient Instance Detection—0
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object LocalizationCode0
3D Object Localization Using 2D Estimates for Computer Vision Applications—0
Polyp-artifact relationship analysis using graph inductive learned representations—0
Adaptive Label Smoothing—0
How semantic and geometric information mutually reinforce each other in ToF object localization—0
Parts-Based Articulated Object Localization in Clutter Using Belief Propagation—0
Entropy Guided Adversarial Model for Weakly Supervised Object Localization—0
Object-Aware Centroid Voting for Monocular 3D Object Detection—0
Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network—0
BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS—0
Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement MapsCode0
Offset Bin Classification Network for Accurate Object Detection—0
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization—0
Salient Object Detection Combining a Self-attention Module and a Feature Pyramid Network—0
Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos—0
How spatial frequencies and color drive object search in real-world scenes: A new eye-movement corpus—0
Rethinking Object Detection in Retail StoresCode0
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object LocalizationCode0
StarNet: towards Weakly Supervised Few-Shot Object DetectionCode0
PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy—0
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference—0
Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning—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