1,497 research outputs found
Towards Accurate Multi-person Pose Estimation in the Wild
We propose a method for multi-person detection and 2-D pose estimation that
achieves state-of-art results on the challenging COCO keypoints task. It is a
simple, yet powerful, top-down approach consisting of two stages.
In the first stage, we predict the location and scale of boxes which are
likely to contain people; for this we use the Faster RCNN detector. In the
second stage, we estimate the keypoints of the person potentially contained in
each proposed bounding box. For each keypoint type we predict dense heatmaps
and offsets using a fully convolutional ResNet. To combine these outputs we
introduce a novel aggregation procedure to obtain highly localized keypoint
predictions. We also use a novel form of keypoint-based Non-Maximum-Suppression
(NMS), instead of the cruder box-level NMS, and a novel form of keypoint-based
confidence score estimation, instead of box-level scoring.
Trained on COCO data alone, our final system achieves average precision of
0.649 on the COCO test-dev set and the 0.643 test-standard sets, outperforming
the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
Further, by using additional in-house labeled data we obtain an even higher
average precision of 0.685 on the test-dev set and 0.673 on the test-standard
set, more than 5% absolute improvement compared to the previous best performing
method on the same dataset.Comment: Paper describing an improved version of the G-RMI entry to the 2016
COCO keypoints challenge (http://image-net.org/challenges/ilsvrc+coco2016).
Camera ready version to appear in the Proceedings of CVPR 201
Enforcing Full Arc Consistency in Asynchronous Forward Bounding Algorithm
The AFB BJ+ DAC* is the latest variant of asynchronous forward bounding algorithms used to solve Distributed Constraint Optimization Problems (DCOPs). It uses Directional Arc Consistency (DAC*) to remove, from domains of a given DCOP, values that do not belong to its optimal solution. However, in some cases, DAC∗ does not remove all suboptimal values, which causes more unnecessary research to reach the optimal solution. In this paper, to clear more and more suboptimal values from a DCOP, we use a higher level of DAC* called Full Directional Arc Consistency (FDAC*). This level is based on reapplying AC* several times, which gives the possibility of making more deletions and thus quickly reaching the optimal solution. Experiments on some benchmarks show that the new algorithm, AFB BJ+ FDAC*, is better in terms of communication load and computation effort
Diversity-Multiplexing Tradeoff of Asynchronous Cooperative Diversity in Wireless Networks
Synchronization of relay nodes is an important and critical issue in
exploiting cooperative diversity in wireless networks. In this paper, two
asynchronous cooperative diversity schemes are proposed, namely, distributed
delay diversity and asynchronous space-time coded cooperative diversity
schemes. In terms of the overall diversity-multiplexing (DM) tradeoff function,
we show that the proposed independent coding based distributed delay diversity
and asynchronous space-time coded cooperative diversity schemes achieve the
same performance as the synchronous space-time coded approach which requires an
accurate symbol-level timing synchronization to ensure signals arriving at the
destination from different relay nodes are perfectly synchronized. This
demonstrates diversity order is maintained even at the presence of asynchronism
between relay node. Moreover, when all relay nodes succeed in decoding the
source information, the asynchronous space-time coded approach is capable of
achieving better DM-tradeoff than synchronous schemes and performs equivalently
to transmitting information through a parallel fading channel as far as the
DM-tradeoff is concerned. Our results suggest the benefits of fully exploiting
the space-time degrees of freedom in multiple antenna systems by employing
asynchronous space-time codes even in a frequency flat fading channel. In
addition, it is shown asynchronous space-time coded systems are able to achieve
higher mutual information than synchronous space-time coded systems for any
finite signal-to-noise-ratio (SNR) when properly selected baseband waveforms
are employed
Distractor-aware Event-based Tracking
Event cameras, or dynamic vision sensors, have recently achieved success from
fundamental vision tasks to high-level vision researches. Due to its ability to
asynchronously capture light intensity changes, event camera has an inherent
advantage to capture moving objects in challenging scenarios including objects
under low light, high dynamic range, or fast moving objects. Thus event camera
are natural for visual object tracking. However, the current event-based
trackers derived from RGB trackers simply modify the input images to event
frames and still follow conventional tracking pipeline that mainly focus on
object texture for target distinction. As a result, the trackers may not be
robust dealing with challenging scenarios such as moving cameras and cluttered
foreground. In this paper, we propose a distractor-aware event-based tracker
that introduces transformer modules into Siamese network architecture (named
DANet). Specifically, our model is mainly composed of a motion-aware network
and a target-aware network, which simultaneously exploits both motion cues and
object contours from event data, so as to discover motion objects and identify
the target object by removing dynamic distractors. Our DANet can be trained in
an end-to-end manner without any post-processing and can run at over 80 FPS on
a single V100. We conduct comprehensive experiments on two large event tracking
datasets to validate the proposed model. We demonstrate that our tracker has
superior performance against the state-of-the-art trackers in terms of both
accuracy and efficiency
A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems
Complete search algorithms are important methods for solving Distributed Constraint Optimization Problems (DCOPs), which generally utilize bounds to prune the search space. However, obtaining high-quality lower bounds is quite expensive since it requires each agent to collect more information aside from its local knowledge, which would cause tremendous traffic overheads. Instead of bothering for bounds, we propose a Bound-Independent Pruning (BIP) technique for existing tree-based complete search algorithms, which can independently reduce the search space only by exploiting local knowledge. Specifically, BIP enables each agent to determine a subspace containing the optimal solution only from its local constraints along with running contexts, which can be further exploited by any search strategies. Furthermore, we present an acceptability testing mechanism to tailor existing tree-based complete search algorithms to search the remaining space returned by BIP when they hold inconsistent contexts. Finally, we prove the correctness of our technique and the experimental results show that BIP can significantly speed up state-of-the-art tree-based complete search algorithms on various standard benchmarks
Simple and Optimal Randomized Fault-Tolerant Rumor Spreading
We revisit the classic problem of spreading a piece of information in a group
of fully connected processors. By suitably adding a small dose of
randomness to the protocol of Gasienic and Pelc (1996), we derive for the first
time protocols that (i) use a linear number of messages, (ii) are correct even
when an arbitrary number of adversarially chosen processors does not
participate in the process, and (iii) with high probability have the
asymptotically optimal runtime of when at least an arbitrarily
small constant fraction of the processors are working. In addition, our
protocols do not require that the system is synchronized nor that all
processors are simultaneously woken up at time zero, they are fully based on
push-operations, and they do not need an a priori estimate on the number of
failed nodes.
Our protocols thus overcome the typical disadvantages of the two known
approaches, algorithms based on random gossip (typically needing a large number
of messages due to their unorganized nature) and algorithms based on fair
workload splitting (which are either not {time-efficient} or require intricate
preprocessing steps plus synchronization).Comment: This is the author-generated version of a paper which is to appear in
Distributed Computing, Springer, DOI: 10.1007/s00446-014-0238-z It is
available online from
http://link.springer.com/article/10.1007/s00446-014-0238-z This version
contains some new results (Section 6
ViZDoom Competitions: Playing Doom from Pixels
This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom
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