  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.
