  In recent years, both online retail and video hosting service are
exponentially growing. In this paper, we explore a new cross-domain task,
Video2Shop, targeting for matching clothes appeared in videos to the exact same
items in online shops. A novel deep neural network, called AsymNet, is proposed
to explore this problem. For the image side, well-established methods are used
to detect and extract features for clothing patches with arbitrary sizes. For
the video side, deep visual features are extracted from detected object regions
in each frame, and further fed into a Long Short-Term Memory (LSTM) framework
for sequence modeling, which captures the temporal dynamics in videos. To
conduct exact matching between videos and online shopping images, LSTM hidden
states, representing the video, and image features, which represent static
object images, are jointly modeled under the similarity network with
reconfigurable deep tree structure. Moreover, an approximate training method is
proposed to achieve the efficiency when training. Extensive experiments
conducted on a large cross-domain dataset have demonstrated the effectiveness
and efficiency of the proposed AsymNet, which outperforms the state-of-the-art
methods.
