1,766 research outputs found
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi
Detecting complex events in user-generated video using concept classifiers
Automatic detection of complex events in user-generated
videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed byconstructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events
Learning Multimodal Latent Attributes
Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from online match reports at a one minute resolution for three main
classes of events (Goal, Yellow/Red Card, and Substitution). As such, the
dataset is easily scalable. These annotations are manually refined to a one
second resolution by anchoring them at a single timestamp following
well-defined soccer rules. With an average of one event every 6.9 minutes, this
dataset focuses on the problem of localizing very sparse events within long
videos. We define the task of spotting as finding the anchors of soccer events
in a video. Making use of recent developments in the realm of generic action
recognition and detection in video, we provide strong baselines for detecting
soccer events. We show that our best model for classifying temporal segments of
length one minute reaches a mean Average Precision (mAP) of 67.8%. For the
spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances
ranging from 5 to 60 seconds. Our dataset and models are available at
https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Automatically recognizing and localizing wide ranges of human actions has
crucial importance for video understanding. Towards this goal, the THUMOS
challenge was introduced in 2013 to serve as a benchmark for action
recognition. Until then, video action recognition, including THUMOS challenge,
had focused primarily on the classification of pre-segmented (i.e., trimmed)
videos, which is an artificial task. In THUMOS 2014, we elevated action
recognition to a more practical level by introducing temporally untrimmed
videos. These also include `background videos' which share similar scenes and
backgrounds as action videos, but are devoid of the specific actions. The three
editions of the challenge organized in 2013--2015 have made THUMOS a common
benchmark for action classification and detection and the annual challenge is
widely attended by teams from around the world.
In this paper we describe the THUMOS benchmark in detail and give an overview
of data collection and annotation procedures. We present the evaluation
protocols used to quantify results in the two THUMOS tasks of action
classification and temporal detection. We also present results of submissions
to the THUMOS 2015 challenge and review the participating approaches.
Additionally, we include a comprehensive empirical study evaluating the
differences in action recognition between trimmed and untrimmed videos, and how
well methods trained on trimmed videos generalize to untrimmed videos. We
conclude by proposing several directions and improvements for future THUMOS
challenges.Comment: Preprint submitted to Computer Vision and Image Understandin
Video Background Music Generation: Dataset, Method and Evaluation
Music is essential when editing videos, but selecting music manually is
difficult and time-consuming. Thus, we seek to automatically generate
background music tracks given video input. This is a challenging task since it
requires plenty of paired videos and music to learn their correspondence.
Unfortunately, there exist no such datasets. To close this gap, we introduce a
dataset, benchmark model, and evaluation metric for video background music
generation. We introduce SymMV, a video and symbolic music dataset, along with
chord, rhythm, melody, and accompaniment annotations. To the best of our
knowledge, it is the first video-music dataset with high-quality symbolic music
and detailed annotations. We also propose a benchmark video background music
generation framework named V-MusProd, which utilizes music priors of chords,
melody, and accompaniment along with video-music relations of semantic, color,
and motion features. To address the lack of objective metrics for video-music
correspondence, we propose a retrieval-based metric VMCP built upon a powerful
video-music representation learning model. Experiments show that with our
dataset, V-MusProd outperforms the state-of-the-art method in both music
quality and correspondence with videos. We believe our dataset, benchmark
model, and evaluation metric will boost the development of video background
music generation
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