SOTAVerified

Region Proposal

Papers

Showing 76100 of 373 papers

TitleStatusHype
Fine-Grained 3D Shape Classification with Hierarchical Part-View AttentionsCode1
PVANET: Deep but Lightweight Neural Networks for Real-time Object DetectionCode0
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point CloudsCode0
On Exploring and Improving Robustness of Scene Text Detection ModelsCode0
Pedestrian Detection with Autoregressive Network PhasesCode0
R2CNN: Rotational Region CNN for Orientation Robust Scene Text DetectionCode0
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural NetworksCode0
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware PriorsCode0
A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time SystemsCode0
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive ConvolutionCode0
Learning regression and verification networks for long-term visual trackingCode0
Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression GroundingCode0
Learning High-quality Proposals for Acne DetectionCode0
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image TranslationCode0
Arbitrary-Oriented Scene Text Detection via Rotation ProposalsCode0
Object Detection based on Region Decomposition and AssemblyCode0
DetectorDetective: Investigating the Effects of Adversarial Examples on Object DetectorsCode0
Breast Cancer Detection Using Convolutional Neural NetworksCode0
Adaptive Reconstruction Network for Weakly Supervised Referring Expression GroundingCode0
Recognizing Object Affordances to Support Scene Reasoning for Manipulation TasksCode0
High Performance Visual Tracking With Siamese Region Proposal NetworkCode0
Detecting Lesion Bounding Ellipses With Gaussian Proposal NetworksCode0
Learning Chained Deep Features and Classifiers for Cascade in Object DetectionCode0
Hierarchical Object Detection with Deep Reinforcement LearningCode0
How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify UncertaintyCode0
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