350 research outputs found
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
Genetic structure and colonisation history of European and UK population of Gammarus pulex
The structure of populations has been studied for many years and there have been three main factors that have been suggested as the cause for present-day distributions of species, those being environment, biology and history. With the use of molecular data and advanced phylogeographic approaches it is now possible to distinguish between the main causes of population structuring. The present study considers the extent of population structure in G. pulex on regional (UK) and large geographic (Europe) scales using studies of molecular genetic (allozymes, mtDNA sequencing and microsatellites) and morphological variation.Molecular analysis of Gammarus pulex in Europe revealed more diversity than previously thought. This was thought to be a consequence of two separate waves of colonisation after the formation of the major drainages in the Miocene. The UK appears to have been colonised once from either the Elbe, Mosel and Rhine drainages separately or cumulatively across the drainage basins late in the Pleistocene before a land bridge connection to mainland Europe was submerged. Limited molecular variation in the UK is thought to be a result of reduced genetic variation in the colonising individuals. This in turn was caused by repeated founder events during population expansion and contraction from European refugia.A detailed analysis of a transplantation experiment in 1950 in the Isle of Man revealed little genetic impoverishment of the introduced population when compared to the source. In contrast, morphological variation increased in the introduced population. Unlike in mainland Europe there was no historical explanation for the diversity recorded (as the introduced population was so young) and, in the absence of fragmentation, speciation and colonisation the contemporary forces of gene flow, selection and limited genetic drift are thought to be the determining factors in population structure
CERiL: Continuous Event-based Reinforcement Learning
This paper explores the potential of event cameras to enable continuous time
reinforcement learning. We formalise this problem where a continuous stream of
unsynchronised observations is used to produce a corresponding stream of output
actions for the environment. This lack of synchronisation enables greatly
enhanced reactivity. We present a method to train on event streams derived from
standard RL environments, thereby solving the proposed continuous time RL
problem. The CERiL algorithm uses specialised network layers which operate
directly on an event stream, rather than aggregating events into quantised
image frames. We show the advantages of event streams over less-frequent RGB
images. The proposed system outperforms networks typically used in RL, even
succeeding at tasks which cannot be solved traditionally. We also demonstrate
the value of our CERiL approach over a standard SNN baseline using event
streams.Comment: 9 pages, 10 figure
Generalizing to New Tasks via One-Shot Compositional Subgoals
The ability to generalize to previously unseen tasks with little to no
supervision is a key challenge in modern machine learning research. It is also
a cornerstone of a future "General AI". Any artificially intelligent agent
deployed in a real world application, must adapt on the fly to unknown
environments. Researchers often rely on reinforcement and imitation learning to
provide online adaptation to new tasks, through trial and error learning.
However, this can be challenging for complex tasks which require many timesteps
or large numbers of subtasks to complete. These "long horizon" tasks suffer
from sample inefficiency and can require extremely long training times before
the agent can learn to perform the necessary longterm planning. In this work,
we introduce CASE which attempts to address these issues by training an
Imitation Learning agent using adaptive "near future" subgoals. These subgoals
are recalculated at each step using compositional arithmetic in a learned
latent representation space. In addition to improving learning efficiency for
standard long-term tasks, this approach also makes it possible to perform
one-shot generalization to previously unseen tasks, given only a single
reference trajectory for the task in a different environment. Our experiments
show that the proposed approach consistently outperforms the previous
state-of-the-art compositional Imitation Learning approach by 30%.Comment: Present at ICRA 2022 "Compositional Robotics: Mathematics and Tools
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
In the current monocular depth research, the dominant approach is to employ
unsupervised training on large datasets, driven by warped photometric
consistency. Such approaches lack robustness and are unable to generalize to
challenging domains such as nighttime scenes or adverse weather conditions
where assumptions about photometric consistency break down.
We propose DeFeat-Net (Depth & Feature network), an approach to
simultaneously learn a cross-domain dense feature representation, alongside a
robust depth-estimation framework based on warped feature consistency. The
resulting feature representation is learned in an unsupervised manner with no
explicit ground-truth correspondences required.
We show that within a single domain, our technique is comparable to both the
current state of the art in monocular depth estimation and supervised feature
representation learning. However, by simultaneously learning features, depth
and motion, our technique is able to generalize to challenging domains,
allowing DeFeat-Net to outperform the current state-of-the-art with around 10%
reduction in all error measures on more challenging sequences such as nighttime
driving
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
"Like night and day" is a commonly used expression to imply that two things
are completely different. Unfortunately, this tends to be the case for current
visual feature representations of the same scene across varying seasons or
times of day. The aim of this paper is to provide a dense feature
representation that can be used to perform localization, sparse matching or
image retrieval, regardless of the current seasonal or temporal appearance.
Recently, there have been several proposed methodologies for deep learning
dense feature representations. These methods make use of ground truth
pixel-wise correspondences between pairs of images and focus on the spatial
properties of the features. As such, they don't address temporal or seasonal
variation. Furthermore, obtaining the required pixel-wise correspondence data
to train in cross-seasonal environments is highly complex in most scenarios.
We propose Deja-Vu, a weakly supervised approach to learning season invariant
features that does not require pixel-wise ground truth data. The proposed
system only requires coarse labels indicating if two images correspond to the
same location or not. From these labels, the network is trained to produce
"similar" dense feature maps for corresponding locations despite environmental
changes. Code will be made available at:
https://github.com/jspenmar/DejaVu_Feature
Kick back & relax: learning to reconstruct the world by watching SlowTV
Self-supervised monocular depth estimation (SS-MDE) has the potential to scale to vast quantities of data. Unfortunately, existing approaches limit themselves to the automotive domain, resulting in models incapable of generalizing to complex environments such as natural or indoor settings. To address this, we propose a large-scale SlowTV dataset curated from YouTube, containing an order of magnitude more data than existing automotive datasets. SlowTV contains 1.7M images from a rich diversity of environments, such as worldwide seasonal hiking, scenic driving and scuba diving. Using this dataset, we train an SS-MDE model that provides zero-shot generalization to a large collection of indoor/outdoor datasets. The resulting model outperforms all existing SSL approaches and closes the gap on supervised SoTA, despite using a more efficient architecture. We additionally introduce a collection of best-practices to further maximize performance and zero-shot generalization. This includes 1) aspect ratio augmentation, 2) camera intrinsic estimation, 3) support frame randomization and 4) flexible motion estimation
Westerlund 1 as a Template for Massive Star Evolution
With a dynamical mass M_dyn ~ 1.3x10e5 M_sun and a lower limit M_cl>5x10e4
M_sun from star counts, Westerlund 1 is the most massive young open cluster
known in the Galaxy and thus the perfect laboratory to study massive star
evolution. We have developed a comprehensive spectral classification scheme for
supergiants based on features in the 6000-9000A range, which allows us to
identify >30 very luminous supergiants in Westerlund 1 and ~100 other less
evolved massive stars, which join the large population of Wolf-Rayet stars
already known. Though detailed studies of these stars are still pending,
preliminary rough estimates suggest that the stars we see are evolving to the
red part of the HR diagram at approximately constant luminosity.Comment: To be published in Proceedings of IAU Symposium 250: Massive Stars as
Cosmic Engines, held in Kaua'i (Hawaii, USA), Dec 2007, edited by F.
Bresolin, P.A. Crowther & J. Puls (Cambridge University Press
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