7,467 research outputs found
EN-BIRTH Data Collector Training - Handbook and Manual
The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains the EN-BIRTH_Trainer's Manual (14 June 2017) and EN-BIRTH_Training Handbook (23 May 2017)
MSPlayer: Multi-Source and multi-Path LeverAged YoutubER
Online video streaming through mobile devices has become extremely popular
nowadays. YouTube, for example, reported that the percentage of its traffic
streaming to mobile devices has soared from 6% to more than 40% over the past
two years. Moreover, people are constantly seeking to stream high quality video
for better experience while often suffering from limited bandwidth. Thanks to
the rapid deployment of content delivery networks (CDNs), popular videos are
now replicated at different sites, and users can stream videos from close-by
locations with low latencies. As mobile devices nowadays are equipped with
multiple wireless interfaces (e.g., WiFi and 3G/4G), aggregating bandwidth for
high definition video streaming has become possible.
We propose a client-based video streaming solution, MSPlayer, that takes
advantage of multiple video sources as well as multiple network paths through
different interfaces. MSPlayer reduces start-up latency and provides high
quality video streaming and robust data transport in mobile scenarios. We
experimentally demonstrate our solution on a testbed and through the YouTube
video service.Comment: accepted to ACM CoNEXT'1
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
First-person vision is gaining interest as it offers a unique viewpoint on
people's interaction with objects, their attention, and even intention.
However, progress in this challenging domain has been relatively slow due to
the lack of sufficiently large datasets. In this paper, we introduce
EPIC-KITCHENS, a large-scale egocentric video benchmark recorded by 32
participants in their native kitchen environments. Our videos depict
nonscripted daily activities: we simply asked each participant to start
recording every time they entered their kitchen. Recording took place in 4
cities (in North America and Europe) by participants belonging to 10 different
nationalities, resulting in highly diverse cooking styles. Our dataset features
55 hours of video consisting of 11.5M frames, which we densely labeled for a
total of 39.6K action segments and 454.3K object bounding boxes. Our annotation
is unique in that we had the participants narrate their own videos (after
recording), thus reflecting true intention, and we crowd-sourced ground-truths
based on these. We describe our object, action and anticipation challenges, and
evaluate several baselines over two test splits, seen and unseen kitchens.
Dataset and Project page: http://epic-kitchens.github.ioComment: European Conference on Computer Vision (ECCV) 2018 Dataset and
Project page: http://epic-kitchens.github.i
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
Video-language pre-training (VLP) has become increasingly important due to
its ability to generalize to various vision and language tasks. However,
existing egocentric VLP frameworks utilize separate video and language encoders
and learn task-specific cross-modal information only during fine-tuning,
limiting the development of a unified system. In this work, we introduce the
second generation of egocentric video-language pre-training (EgoVLPv2), a
significant improvement from the previous generation, by incorporating
cross-modal fusion directly into the video and language backbones. EgoVLPv2
learns strong video-text representation during pre-training and reuses the
cross-modal attention modules to support different downstream tasks in a
flexible and efficient manner, reducing fine-tuning costs. Moreover, our
proposed fusion in the backbone strategy is more lightweight and
compute-efficient than stacking additional fusion-specific layers. Extensive
experiments on a wide range of VL tasks demonstrate the effectiveness of
EgoVLPv2 by achieving consistent state-of-the-art performance over strong
baselines across all downstream. Our project page can be found at
https://shramanpramanick.github.io/EgoVLPv2/.Comment: Published in ICCV 202
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