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 551–600 of 617 papers

TitleStatusHype
Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch ClassificationCode0
Does Thermal Really Always Matter for RGB-T Salient Object Detection?Code0
Count-ception: Counting by Fully Convolutional Redundant CountingCode0
TeD-Loc: Text Distillation for Weakly Supervised Object LocalizationCode0
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote SensingCode0
Tell Me Where to Look: Guided Attention Inference NetworkCode0
Adversarial Normalization: I Can Visualize Everything (ICE)Code0
BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised ApplicationsCode0
RON: Reverse Connection with Objectness Prior Networks for Object DetectionCode0
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgettingCode0
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation dataCode0
Min-Entropy Latent Model for Weakly Supervised Object DetectionCode0
Texture or Semantics? Vision-Language Models Get Lost in Font RecognitionCode0
GraPix: Exploring Graph Modularity Optimization for Unsupervised Pixel ClusteringCode0
Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient ImagesCode0
Grid R-CNNCode0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and InterpretabilityCode0
Dilated Residual NetworksCode0
PEEKABOO: Hiding parts of an image for unsupervised object localizationCode0
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving CameraCode0
Video Anomaly Detection by Estimating Likelihood of RepresentationsCode0
Personal Fixations-Based Object Segmentation with Object Localization and Boundary PreservationCode0
Sketch-based Video Object LocalizationCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action LocalizationCode0
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object LocalizationCode0
PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI LocalizationCode0
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A SurveyCode0
Holistic 3D Scene Parsing and Reconstruction from a Single RGB ImageCode0
ScanRefer: 3D Object Localization in RGB-D Scans using Natural LanguageCode0
Scene-Text Oriented Reffering Expression ComprehensionCode0
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object LocalizationCode0
Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoderCode0
IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured TrafficCode0
Slack and Margin Rescaling as Convex Extensions of Supermodular FunctionsCode0
DeNet: Scalable Real-time Object Detection with Directed Sparse SamplingCode0
Adversarial Complementary Learning for Weakly Supervised Object LocalizationCode0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network ModelsCode0
DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance SynergyCode0
A Simple Single-Scale Vision Transformer for Object Localization and Instance SegmentationCode0
Improving Object Localization with Fitness NMS and Bounded IoU LossCode0
A Smoother Way to Train Structured Prediction ModelsCode0
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object DetectionCode0
DANet: Divergent Activation for Weakly Supervised Object LocalizationCode0
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object LocalizationCode0
DAP: Detection-Aware Pre-training with Weak SupervisionCode0
Multispectral Detection Transformer with Infrared-Centric Sensor FusionCode0
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object LocalizationCode0
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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