438 research outputs found
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
Object detection is an important and challenging problem in computer vision.
Although the past decade has witnessed major advances in object detection in
natural scenes, such successes have been slow to aerial imagery, not only
because of the huge variation in the scale, orientation and shape of the object
instances on the earth's surface, but also due to the scarcity of
well-annotated datasets of objects in aerial scenes. To advance object
detection research in Earth Vision, also known as Earth Observation and Remote
Sensing, we introduce a large-scale Dataset for Object deTection in Aerial
images (DOTA). To this end, we collect aerial images from different
sensors and platforms. Each image is of the size about 4000-by-4000 pixels and
contains objects exhibiting a wide variety of scales, orientations, and shapes.
These DOTA images are then annotated by experts in aerial image interpretation
using common object categories. The fully annotated DOTA images contains
instances, each of which is labeled by an arbitrary (8 d.o.f.)
quadrilateral To build a baseline for object detection in Earth Vision, we
evaluate state-of-the-art object detection algorithms on DOTA. Experiments
demonstrate that DOTA well represents real Earth Vision applications and are
quite challenging.Comment: Accepted to CVPR 201
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
This article presents Holistically-Attracted Wireframe Parsing (HAWP), a
method for geometric analysis of 2D images containing wireframes formed by line
segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT)
field representation that encodes line segments using a closed-form 4D
geometric vector field. The proposed HAWP consists of three sequential
components empowered by end-to-end and HAT-driven designs: (1) generating a
dense set of line segments from HAT fields and endpoint proposals from
heatmaps, (2) binding the dense line segments to sparse endpoint proposals to
produce initial wireframes, and (3) filtering false positive proposals through
a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that
captures the co-occurrence between endpoint proposals and HAT fields for better
verification. Thanks to our novel designs, HAWPv2 shows strong performance in
fully supervised learning, while HAWPv3 excels in self-supervised learning,
achieving superior repeatability scores and efficient training (24 GPU hours on
a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe
parsing in out-of-distribution images without providing ground truth labels of
wireframes.Comment: Journal extension of arXiv:2003.01663; Accepted by IEEE TPAMI; Code
is available at https://github.com/cherubicxn/haw
Learning Regional Attraction for Line Segment Detection
This paper presents regional attraction of line segment maps, and hereby
poses the problem of line segment detection (LSD) as a problem of region
coloring. Given a line segment map, the proposed regional attraction first
establishes the relationship between line segments and regions in the image
lattice. Based on this, the line segment map is equivalently transformed to an
attraction field map (AFM), which can be remapped to a set of line segments
without loss of information. Accordingly, we develop an end-to-end framework to
learn attraction field maps for raw input images, followed by a squeeze module
to detect line segments. Apart from existing works, the proposed detector
properly handles the local ambiguity and does not rely on the accurate
identification of edge pixels. Comprehensive experiments on the Wireframe
dataset and the YorkUrban dataset demonstrate the superiority of our method. In
particular, we achieve an F-measure of 0.831 on the Wireframe dataset,
advancing the state-of-the-art performance by 10.3 percent.Comment: Accepted to IEEE TPAMI. arXiv admin note: text overlap with
arXiv:1812.0212
Holistically-Attracted Wireframe Parsing
This paper presents a fast and parsimonious parsing method to accurately and
robustly detect a vectorized wireframe in an input image with a single forward
pass. The proposed method is end-to-end trainable, consisting of three
components: (i) line segment and junction proposal generation, (ii) line
segment and junction matching, and (iii) line segment and junction
verification. For computing line segment proposals, a novel exact dual
representation is proposed which exploits a parsimonious geometric
reparameterization for line segments and forms a holistic 4-dimensional
attraction field map for an input image. Junctions can be treated as the
"basins" in the attraction field. The proposed method is thus called
Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed
method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban
dataset. On both benchmarks, it obtains state-of-the-art performance in terms
of accuracy and efficiency. For example, on the Wireframe dataset, compared to
the previous state-of-the-art method L-CNN, it improves the challenging mean
structural average precision (msAP) by a large margin ( absolute
improvements) and achieves 29.5 FPS on single GPU ( relative
improvement). A systematic ablation study is performed to further justify the
proposed method.Comment: Accepted by CVPR 202
A huge-amplitude white-light superflare on a L0 brown dwarf discovered by GWAC survey
White-light superflares from ultra cool stars are thought to be resulted from
magnetic reconnection, but the magnetic dynamics in a fully convective star is
not clear yet. In this paper, we report a stellar superflare detected with the
Ground Wide Angle Camera (GWAC), along with rapid follow-ups with the F60A,
Xinglong 2.16m and LCOGT telescopes. The effective temperature of the
counterpart is estimated to be K by the BT-Settl model,
corresponding to a spectral type of L0. The band light curve can be modeled
as a sum of three exponential decay components, where the impulsive component
contributes a fraction of 23\% of the total energy, while the gradual and the
shallower decay phases emit 42\% and 35\% of the total energy, respectively.
The strong and variable Balmer narrow emission lines indicate the large
amplitude flare is resulted from magnetic activity. The bolometric energy
released is about ergs, equivalent to an energy release in a
duration of 143.7 hours at its quiescent level. The amplitude of mag ( or mag), placing it one of the highest amplitudes of
any ultra cool star recorded with excellent temporal resolution. We argue that
a stellar flare with such rapidly decaying and huge amplitude at distances
greater than 1 kpc may be false positive in searching for counterparts of
catastrophic events such as gravitational wave events or gamma-ray bursts,
which are valuable in time-domain astronomy and should be given more attention.Comment: 9 pages, 5 figures, 1 table, MNRAS accepte
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