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 with no code
Relation Graph Network for 3D Object Detection in Point Clouds
Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images.
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
A New Target-specific Object Proposal Generation Method for Visual Tracking
Then, we apply the proposed TOPG method to the task of visual tracking and propose a TOPG-based tracker (called as TOPGT), where TOPG is used as a sample selection strategy to select a small number of high-quality target candidates from the generated object proposals.
Object cosegmentation using deep Siamese network
Object cosegmentation addresses the problem of discovering similar objects from multiple images and segmenting them as foreground simultaneously.
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
These ConvNet based edge detectors have approached human level performance on standard benchmarks.
SalProp: Salient object proposals via aggregated edge cues
In this paper, we propose a novel object proposal generation scheme by formulating a graph-based salient edge classification framework that utilizes the edge context.
Object Discovery via Cohesion Measurement
Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix.
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.
Boundary-aware Instance Segmentation
In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal.
Learning to Segment Object Candidates via Recursive Neural Networks
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images.
