22,126 research outputs found
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
Unsupervised Learning of Visual Representations using Videos
Is strong supervision necessary for learning a good visual representation? Do
we really need millions of semantically-labeled images to train a Convolutional
Neural Network (CNN)? In this paper, we present a simple yet surprisingly
powerful approach for unsupervised learning of CNN. Specifically, we use
hundreds of thousands of unlabeled videos from the web to learn visual
representations. Our key idea is that visual tracking provides the supervision.
That is, two patches connected by a track should have similar visual
representation in deep feature space since they probably belong to the same
object or object part. We design a Siamese-triplet network with a ranking loss
function to train this CNN representation. Without using a single image from
ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train
an ensemble of unsupervised networks that achieves 52% mAP (no bounding box
regression). This performance comes tantalizingly close to its
ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4%. We
also show that our unsupervised network can perform competitively in other
tasks such as surface-normal estimation
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