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
Small, but important: Traffic light proposals for detecting small traffic lights and beyond
Traffic light detection is a challenging problem in the context of self-driving cars and driver assistance systems.
Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask
Our evaluation in an object proposal generation framework shows that our adapted AttentionMask system is robust to image degradations, generalizes well to unseen types of surgeries, and copes well with small instruments.
Localizing Small Apples in Complex Apple Orchard Environments
Since the apples are very small objects in such scenarios, we tackle this problem by adapting the object proposal generation system AttentionMask that focuses on small objects.
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues
Firstly, we analyze CLIP for unsupervised open-category proposal generation and design an objectness score based on our empirical analysis on proposal selection.
DeepFH Segmentations for Superpixel-based Object Proposal Refinement
Class-agnostic object proposal generation is an important first step in many object detection pipelines.
3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds
Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world.
You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking
In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames.
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance.
What leads to generalization of object proposals?
It is lucrative to train a good proposal model, that generalizes to unseen classes.
Real-time 3D object proposal generation and classification under limited processing resources
The task of detecting 3D objects is important to various robotic applications.
