  Cross-media retrieval is a research hotspot in multimedia area, which aims to
perform retrieval across different media types such as image and text. The
performance of existing methods usually relies on labeled data for model
training. However, cross-media data is very labor consuming to collect and
label, so how to transfer valuable knowledge in existing data to new data is a
key problem towards application. For achieving the goal, this paper proposes
deep cross-media knowledge transfer (DCKT) approach, which transfers knowledge
from a large-scale cross-media dataset to promote the model training on another
small-scale cross-media dataset. The main contributions of DCKT are: (1)
Two-level transfer architecture is proposed to jointly minimize the media-level
and correlation-level domain discrepancies, which allows two important and
complementary aspects of knowledge to be transferred: intra-media semantic and
inter-media correlation knowledge. It can enrich the training information and
boost the retrieval accuracy. (2) Progressive transfer mechanism is proposed to
iteratively select training samples with ascending transfer difficulties, via
the metric of cross-media domain consistency with adaptive feedback. It can
drive the transfer process to gradually reduce vast cross-media domain
discrepancy, so as to enhance the robustness of model training. For verifying
the effectiveness of DCKT, we take the largescale dataset XMediaNet as source
domain, and 3 widelyused datasets as target domain for cross-media retrieval.
Experimental results show that DCKT achieves promising improvement on retrieval
accuracy.
