Object Proposal Generation
Object proposal generation is a preprocessing technique that has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images.
State-of-the-art leaderboards
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Greatest papers with code
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
CVPR 2019
• sshaoshuai/PointRCNN
• 
In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
Multi-View 3D Object Detection Network for Autonomous Driving
CVPR 2017
• bostondiditeam/MV3D
• 
We encode the sparse 3D point cloud with a compact multi-view representation.
3D OBJECT DETECTION AUTONOMOUS DRIVING OBJECT PROPOSAL GENERATION
Recurrent Pixel Embedding for Instance Grouping
CVPR 2018 • aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping
We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components.
BOUNDARY DETECTION INSTANCE SEGMENTATION OBJECT PROPOSAL GENERATION SEMANTIC SEGMENTATION
CASENet: Deep Category-Aware Semantic Edge Detection
To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.
SOTA for Edge Detection on SBD
EDGE DETECTION OBJECT PROPOSAL GENERATION SEMANTIC SEGMENTATION
Semantic Instance Segmentation via Deep Metric Learning
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.
#2 best model for
Object Proposal Generation
on PASCAL VOC 2012, 60 proposals per image
INSTANCE SEGMENTATION METRIC LEARNING OBJECT PROPOSAL GENERATION SEMANTIC SEGMENTATION
Deep Learning for Generic Object Detection: A Survey
Generic object detection, aiming at locating object instances from a large number of predefined categories in natural images, is one of the most fundamental and challenging problems in computer vision.