Object Proposal Generation
20 papers with code • 4 benchmarks • 4 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 )
MS COCO
KITTI
Comic2k
CocoDoom
Latest papers
Semantic Edge Detection with Diverse Deep Supervision
arsenal9971/shearlet_semantic_edge
•
•
Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
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.
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.
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.
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.
Multi-View 3D Object Detection Network for Autonomous Driving
bostondiditeam/MV3D
•
•
CVPR 2017
We encode the sparse 3D point cloud with a compact multi-view representation.
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.
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.
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.
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).
