Object Proposal Generation
21 papers with code • 2 benchmarks • 3 datasets
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.
( Image credit: Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation )
Benchmarks
These leaderboards are used to track progress in Object Proposal Generation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MS COCO
Comic2k
CocoDoom
Most implemented papers
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
sshaoshuai/PointRCNN
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CVPR 2019
In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
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.
Multi-View 3D Object Detection Network for Autonomous Driving
bostondiditeam/MV3D
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CVPR 2017
We encode the sparse 3D point cloud with a compact multi-view representation.
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.
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG).
Convolutional Channel Features
With the combination of CNN features and boosting forest, CCF benefits from the richer capacity in feature representation compared with channel features, as well as lower cost in computation and storage compared with end-to-end CNN methods.
Seq-NMS for Video Object Detection
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.
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.
Object proposal generation applying the distance dependent Chinese restaurant process
In application domains such as robotics, it is useful to represent the uncertainty related to the robot's belief about the state of its environment.
