17 research outputs found
Learn to Propagate Reliably on Noisy Affinity Graphs
Recent works have shown that exploiting unlabeled data through label
propagation can substantially reduce the labeling cost, which has been a
critical issue in developing visual recognition models. Yet, how to propagate
labels reliably, especially on a dataset with unknown outliers, remains an open
question. Conventional methods such as linear diffusion lack the capability of
handling complex graph structures and may perform poorly when the seeds are
sparse. Latest methods based on graph neural networks would face difficulties
on performance drop as they scale out to noisy graphs. To overcome these
difficulties, we propose a new framework that allows labels to be propagated
reliably on large-scale real-world data. This framework incorporates (1) a
local graph neural network to predict accurately on varying local structures
while maintaining high scalability, and (2) a confidence-based path scheduler
that identifies outliers and moves forward the propagation frontier in a
prudent way. Experiments on both ImageNet and Ms-Celeb-1M show that our
confidence guided framework can significantly improve the overall accuracies of
the propagated labels, especially when the graph is very noisy.Comment: 14 pages, 7 figures, ECCV 202
MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond
Neural radiance fields (NeRF) and its subsequent variants have led to
remarkable progress in neural rendering. While most of recent neural rendering
works focus on objects and small-scale scenes, developing neural rendering
methods for city-scale scenes is of great potential in many real-world
applications. However, this line of research is impeded by the absence of a
comprehensive and high-quality dataset, yet collecting such a dataset over real
city-scale scenes is costly, sensitive, and technically difficult. To this end,
we build a large-scale, comprehensive, and high-quality synthetic dataset for
city-scale neural rendering researches. Leveraging the Unreal Engine 5 City
Sample project, we develop a pipeline to easily collect aerial and street city
views, accompanied by ground-truth camera poses and a range of additional data
modalities. Flexible controls over environmental factors like light, weather,
human and car crowd are also available in our pipeline, supporting the need of
various tasks covering city-scale neural rendering and beyond. The resulting
pilot dataset, MatrixCity, contains 67k aerial images and 452k street images
from two city maps of total size . On top of MatrixCity, a thorough
benchmark is also conducted, which not only reveals unique challenges of the
task of city-scale neural rendering, but also highlights potential improvements
for future works. The dataset and code will be publicly available at our
project page: https://city-super.github.io/matrixcity/.Comment: Accepted to ICCV 2023. Project page:
$\href{https://city-super.github.io/matrixcity/}{this\, https\, URL}
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images
This paper presents OmniCity, a new dataset for omnipotent city understanding
from multi-level and multi-view images. More precisely, the OmniCity contains
multi-view satellite images as well as street-level panorama and mono-view
images, constituting over 100K pixel-wise annotated images that are
well-aligned and collected from 25K geo-locations in New York City. To
alleviate the substantial pixel-wise annotation efforts, we propose an
efficient street-view image annotation pipeline that leverages the existing
label maps of satellite view and the transformation relations between different
views (satellite, panorama, and mono-view). With the new OmniCity dataset, we
provide benchmarks for a variety of tasks including building footprint
extraction, height estimation, and building plane/instance/fine-grained
segmentation. Compared with the existing multi-level and multi-view benchmarks,
OmniCity contains a larger number of images with richer annotation types and
more views, provides more benchmark results of state-of-the-art models, and
introduces a novel task for fine-grained building instance segmentation on
street-level panorama images. Moreover, OmniCity provides new problem settings
for existing tasks, such as cross-view image matching, synthesis, segmentation,
detection, etc., and facilitates the developing of new methods for large-scale
city understanding, reconstruction, and simulation. The OmniCity dataset as
well as the benchmarks will be available at
https://city-super.github.io/omnicity
Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production
Amateurs working on mini-films and short-form videos usually spend lots of
time and effort on the multi-round complicated process of setting and adjusting
scenes, plots, and cameras to deliver satisfying video shots. We present
Virtual Dynamic Storyboard (VDS) to allow users storyboarding shots in virtual
environments, where the filming staff can easily test the settings of shots
before the actual filming. VDS runs on a "propose-simulate-discriminate" mode:
Given a formatted story script and a camera script as input, it generates
several character animation and camera movement proposals following predefined
story and cinematic rules to allow an off-the-shelf simulation engine to render
videos. To pick up the top-quality dynamic storyboard from the candidates, we
equip it with a shot ranking discriminator based on shot quality criteria
learned from professional manual-created data. VDS is comprehensively validated
via extensive experiments and user studies, demonstrating its efficiency,
effectiveness, and great potential in assisting amateur video production.Comment: Project page: https://virtualfilmstudio.github.io
Gene discovery in EST sequences from the wheat leaf rust fungus Puccinia triticina sexual spores, asexual spores and haustoria, compared to other rust and corn smut fungi
© 2011 Xu et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.DOI: 10.1186/1471-2164-12-161Background.Rust fungi are biotrophic basidiomycete plant pathogens that cause major diseases on plants and trees world-wide, affecting agriculture and forestry. Their biotrophic nature precludes many established molecular genetic manipulations and lines of research. The generation of genomic resources for these microbes is leading to novel insights into biology such as interactions with the hosts and guiding directions for breakthrough research in plant pathology. Results. To support gene discovery and gene model verification in the genome of the wheat leaf rust fungus, Puccinia triticina (Pt), we have generated Expressed Sequence Tags (ESTs) by sampling several life cycle stages. We focused on several spore stages and isolated haustorial structures from infected wheat, generating 17,684 ESTs. We produced sequences from both the sexual (pycniospores, aeciospores and teliospores) and asexual (germinated urediniospores) stages of the life cycle. From pycniospores and aeciospores, produced by infecting the alternate host, meadow rue (Thalictrum speciosissimum), 4,869 and 1,292 reads were generated, respectively. We generated 3,703 ESTs from teliospores produced on the senescent primary wheat host. Finally, we generated 6,817 reads from haustoria isolated from infected wheat as well as 1,003 sequences from germinated urediniospores. Along with 25,558 previously generated ESTs, we compiled a database of 13,328 non-redundant sequences (4,506 singlets and 8,822 contigs). Fungal genes were predicted using the EST version of the self-training GeneMarkS algorithm. To refine the EST database, we compared EST sequences by BLASTN to a set of 454 pyrosequencing-generated contigs and Sanger BAC-end sequences derived both from the Pt genome, and to ESTs and genome reads from wheat. A collection of 6,308 fungal genes was identified and compared to sequences of the cereal rusts, Puccinia graminis f. sp. tritici (Pgt) and stripe rust, P. striiformis f. sp. tritici (Pst), and poplar leaf rust Melampsora species, and the corn smut fungus, Ustilago maydis (Um). While extensive homologies were found, many genes appeared novel and species-specific; over 40% of genes did not match any known sequence in existing databases. Focusing on spore stages, direct comparison to Um identified potential functional homologs, possibly allowing heterologous functional analysis in that model fungus. Many potentially secreted protein genes were identified by similarity searches against genes and proteins of Pgt and Melampsora spp., revealing apparent orthologs. Conclusions. The current set of Pt unigenes contributes to gene discovery in this major cereal pathogen and will be invaluable for gene model verification in the genome sequence
Colluding RF Fingerprint Impersonation Attack Based on Generative Adversarial Network
Radio frequency fingerprint (RFF) is an effective way to improve the security of wireless communications. Existing research mainly focused on the classification capability and the robustness of RFFs but overlooked malicious attacks. In this paper, a colluding impersonation attack framework is proposed to emulate the RFF of legitimate users. A colluding attacker is introduced to observe the signal features of the impersonation attacker and the legitimate user and compare their difference. The difference is fed back to the impersonation attacker to help improve its RFF impersonation method. With this idea, the impersonation attack is realized by the Generative Adversarial Network (GAN) structure. The RFF impersonation is formulated as the generator whose objective is to output the signal with RFF similar to the legitimate user, viewed from the colluding attacker's perspective. Simulation results show that the proposed method can effectively impersonate the legitimate user's RFF under the dynamic block fading channel
BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
Neural radiance fields (NeRF) has achieved outstanding performance in
modeling 3D objects and controlled scenes, usually under a single scale. In
this work, we focus on multi-scale cases where large changes in imagery are
observed at drastically different scales. This scenario vastly exists in
real-world 3D environments, such as city scenes, with views ranging from
satellite level that captures the overview of a city, to ground level imagery
showing complex details of an architecture; and can also be commonly identified
in landscape and delicate minecraft 3D models. The wide span of viewing
positions within these scenes yields multi-scale renderings with very different
levels of detail, which poses great challenges to neural radiance field and
biases it towards compromised results. To address these issues, we introduce
BungeeNeRF, a progressive neural radiance field that achieves level-of-detail
rendering across drastically varied scales. Starting from fitting distant views
with a shallow base block, as training progresses, new blocks are appended to
accommodate the emerging details in the increasingly closer views. The strategy
progressively activates high-frequency channels in NeRF's positional encoding
inputs and successively unfolds more complex details as the training proceeds.
We demonstrate the superiority of BungeeNeRF in modeling diverse multi-scale
scenes with drastically varying views on multiple data sources (city models,
synthetic, and drone captured data) and its support for high-quality rendering
in different levels of detail.Comment: Accepted to ECCV22; Previous version: CityNeRF: Building NeRF at City
Scale; Project page can be found in https://city-super.github.io/cityner