175,531 research outputs found
Learning to Segment Every Thing
Most methods for object instance segmentation require all training examples
to be labeled with segmentation masks. This requirement makes it expensive to
annotate new categories and has restricted instance segmentation models to ~100
well-annotated classes. The goal of this paper is to propose a new partially
supervised training paradigm, together with a novel weight transfer function,
that enables training instance segmentation models on a large set of categories
all of which have box annotations, but only a small fraction of which have mask
annotations. These contributions allow us to train Mask R-CNN to detect and
segment 3000 visual concepts using box annotations from the Visual Genome
dataset and mask annotations from the 80 classes in the COCO dataset. We
evaluate our approach in a controlled study on the COCO dataset. This work is a
first step towards instance segmentation models that have broad comprehension
of the visual world
Panoptic Segmentation
We propose and study a task we name panoptic segmentation (PS). Panoptic
segmentation unifies the typically distinct tasks of semantic segmentation
(assign a class label to each pixel) and instance segmentation (detect and
segment each object instance). The proposed task requires generating a coherent
scene segmentation that is rich and complete, an important step toward
real-world vision systems. While early work in computer vision addressed
related image/scene parsing tasks, these are not currently popular, possibly
due to lack of appropriate metrics or associated recognition challenges. To
address this, we propose a novel panoptic quality (PQ) metric that captures
performance for all classes (stuff and things) in an interpretable and unified
manner. Using the proposed metric, we perform a rigorous study of both human
and machine performance for PS on three existing datasets, revealing
interesting insights about the task. The aim of our work is to revive the
interest of the community in a more unified view of image segmentation.Comment: accepted to CVPR 201
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does not predict any overlapping instances. Moreover, we are able to segment
both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with
image-level tags. We obtain state-of-the-art results on Pascal VOC, for both
full and weak supervision (which achieves about 95% of fully-supervised
performance). Furthermore, we present the first weakly-supervised results on
Cityscapes for both semantic- and instance-segmentation. Finally, we use our
weakly supervised framework to analyse the relationship between annotation
quality and predictive performance, which is of interest to dataset creators.Comment: ECCV 2018. The first two authors contributed equall
Spartan Daily November 3, 2009
Volume 133, Issue 34https://scholarworks.sjsu.edu/spartandaily/1301/thumbnail.jp
Emergency Management Training for Transportation Agencies
State transportation agencies have a variety of responsibilities related to emergency management. Field personnel manage events--from day-to-day emergencies to disasters--using the Incident Command System (ICS) as their organizational basis. At the headquarters level, the Emergency Operations Center (EOC) coordinates the use of resources across the department and its districts, with other state departments and agencies, and through the federal Emergency Support Function 1. District-level EOCs coordinate with the department. In extreme events, the transportation department may only be able to deliver limited essential services in austere conditions, so a continuity of operations/ continuity of government plan (COOP/COG) is essential. This research applied the principles of andragogy to deliver ICS field level training, EOC training and COOP/COG training to state transportation agency’s staff in all districts and at headquarters. The data supports the need for adult-oriented methods in emergency management training
- …