258,870 research outputs found
Understanding Flow Performance in the Wild
Abstract-Recent Internet studies have reported on continued traffic growth and popularity of web-based applications. Any adverse impact that these observed trends may have on Internet traffic flows can result in sub par performance, which in turn results in unsatisfactory user experience. Leveraging data collected at a major content distribution network (CDN), we investigate flow-level performance in the wild. We observe that packet losses differ widely across flows of different sizes, and even for flows of similar size. To shed light on these observations, we rely on a controlled testbed setup with advanced instrumentation via NetFPGA cards. We highlight the key factors which can degrade flow-performance across different network loads and flow-size distributions. We find that packet losses do not affect all flows similarly. Depending on the network load, some flows either suffer from significantly more drops (unhappy flows) or significantly less drops than the average loss rate (happy flows). Very few flows actually observe a loss rate similar to the average loss rate. Therefore, any single flow is very unlikely to observe the global packet loss process. Furthermore, we find that some flows are burstier than others as indicated by their average congestion window
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 430 15-minute video clips, where actions are localized in space and
time, resulting in 1.58M action labels with multiple labels per person
occurring frequently. The key characteristics of our dataset are: (1) the
definition of atomic visual actions, rather than composite actions; (2) precise
spatio-temporal annotations with possibly multiple annotations for each person;
(3) exhaustive annotation of these atomic actions over 15-minute video clips;
(4) people temporally linked across consecutive segments; and (5) using movies
to gather a varied set of action representations. This departs from existing
datasets for spatio-temporal action recognition, which typically provide sparse
annotations for composite actions in short video clips. We will release the
dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic
difficulty of action recognition. To benchmark this, we present a novel
approach for action localization that builds upon the current state-of-the-art
methods, and demonstrates better performance on JHMDB and UCF101-24 categories.
While setting a new state of the art on existing datasets, the overall results
on AVA are low at 15.6% mAP, underscoring the need for developing new
approaches for video understanding.Comment: To appear in CVPR 2018. Check dataset page
https://research.google.com/ava/ for detail
The ecology of exercise: mechanisms underlying Individual variation in behavior, activity, and performance: an introduction to symposium
Wild animals often engage in intense physical activity while performing tasks vital for their survival and reproduction associated with foraging, avoiding predators, fighting, providing parental care, and migrating. In this theme issue we consider how viewing these tasks as “exercise”—analogous to that performed by human athletes—may help provide insight into the mechanisms underlying individual variation in these types of behaviors and the importance of physical activity in an ecological context. In this article and throughout this issue, we focus on four key questions relevant to the study of behavioral ecology that may be addressed by studying wild animal behavior from the perspective of exercise physiology: (1) How hard do individual animals work in response to ecological (or evolutionary) demands?; (2) Do lab-based studies of activity provide good models for understanding activity in free-living animals and individual variation in traits?; (3) Can animals work too hard during “routine” activities?; and (4) Can paradigms of “exercise” and “training” be applied to free-living animals? Attempts to address these issues are currently being facilitated by rapid technological developments associated with physiological measurements and the remote tracking of wild animals, to provide mechanistic insights into the behavior of free-ranging animals at spatial and temporal scales that were previously impossible. We further suggest that viewing the behaviors of non-human animals in terms of the physical exercise performed will allow us to fully take advantage of these technological advances, draw from knowledge and conceptual frameworks already in use by human exercise physiologists, and identify key traits that constrain performance and generate variation in performance among individuals. It is our hope that, by highlighting mechanisms of behavior and performance, the articles in this issue will spur on further synergies between physiologists and ecologists, to take advantage of emerging cross-disciplinary perspectives and technologies
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
Webly-supervised learning has recently emerged as an alternative paradigm to
traditional supervised learning based on large-scale datasets with manual
annotations. The key idea is that models such as CNNs can be learned from the
noisy visual data available on the web. In this work we aim to exploit web data
for video understanding tasks such as action recognition and detection. One of
the main problems in webly-supervised learning is cleaning the noisy labeled
data from the web. The state-of-the-art paradigm relies on training a first
classifier on noisy data that is then used to clean the remaining dataset. Our
key insight is that this procedure biases the second classifier towards samples
that the first one understands. Here we train two independent CNNs, a RGB
network on web images and video frames and a second network using temporal
information from optical flow. We show that training the networks independently
is vastly superior to selecting the frames for the flow classifier by using our
RGB network. Moreover, we show benefits in enriching the training set with
different data sources from heterogeneous public web databases. We demonstrate
that our framework outperforms all other webly-supervised methods on two public
benchmarks, UCF-101 and Thumos'14.Comment: Submitted to CVIU SI: Computer Vision and the We
Flowing ConvNets for Human Pose Estimation in Videos
The objective of this work is human pose estimation in videos, where multiple
frames are available. We investigate a ConvNet architecture that is able to
benefit from temporal context by combining information across the multiple
frames using optical flow.
To this end we propose a network architecture with the following novelties:
(i) a deeper network than previously investigated for regressing heatmaps; (ii)
spatial fusion layers that learn an implicit spatial model; (iii) optical flow
is used to align heatmap predictions from neighbouring frames; and (iv) a final
parametric pooling layer which learns to combine the aligned heatmaps into a
pooled confidence map.
We show that this architecture outperforms a number of others, including one
that uses optical flow solely at the input layers, one that regresses joint
coordinates directly, and one that predicts heatmaps without spatial fusion.
The new architecture outperforms the state of the art by a large margin on
three video pose estimation datasets, including the very challenging Poses in
the Wild dataset, and outperforms other deep methods that don't use a graphical
model on the single-image FLIC benchmark (and also Chen & Yuille and Tompson et
al. in the high precision region).Comment: ICCV'1
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