20 research outputs found
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
Managemen
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
Generating temporal action proposals remains a very challenging problem,
where the main issue lies in predicting precise temporal proposal boundaries
and reliable action confidence in long and untrimmed real-world videos. In this
paper, we propose an efficient and unified framework to generate temporal
action proposals named Dense Boundary Generator (DBG), which draws inspiration
from boundary-sensitive methods and implements boundary classification and
action completeness regression for densely distributed proposals. In
particular, the DBG consists of two modules: Temporal boundary classification
(TBC) and Action-aware completeness regression (ACR). The TBC aims to provide
two temporal boundary confidence maps by low-level two-stream features, while
the ACR is designed to generate an action completeness score map by high-level
action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to
encode RGB and optical flow information, which helps to capture discriminative
boundary and actionness features. Extensive experiments on popular benchmarks
ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the
state-of-the-art proposal generator (e.g., MGG and BMN). Our code will be made
available upon publication.Comment: Accepted by AAAI 2020. Ranked No. 1 on ActivityNet Challenge 2019 on
Temporal Action Proposals
(http://activity-net.org/challenges/2019/evaluation.html
Zero-Shot Multi-View Indoor Localization via Graph Location Networks
Indoor localization is a fundamental problem in location-based applications.
Current approaches to this problem typically rely on Radio Frequency
technology, which requires not only supporting infrastructures but human
efforts to measure and calibrate the signal. Moreover, data collection for all
locations is indispensable in existing methods, which in turn hinders their
large-scale deployment. In this paper, we propose a novel neural network based
architecture Graph Location Networks (GLN) to perform infrastructure-free,
multi-view image based indoor localization. GLN makes location predictions
based on robust location representations extracted from images through
message-passing networks. Furthermore, we introduce a novel zero-shot indoor
localization setting and tackle it by extending the proposed GLN to a dedicated
zero-shot version, which exploits a novel mechanism Map2Vec to train
location-aware embeddings and make predictions on novel unseen locations. Our
extensive experiments show that the proposed approach outperforms
state-of-the-art methods in the standard setting, and achieves promising
accuracy even in the zero-shot setting where data for half of the locations are
not available. The source code and datasets are publicly available at
https://github.com/coldmanck/zero-shot-indoor-localization-release.Comment: Accepted at ACM MM 2020. 10 pages, 7 figures. Code and datasets
available at
https://github.com/coldmanck/zero-shot-indoor-localization-releas