25,037 research outputs found
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
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
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