  In this work, we introduce an important but still unexplored research task --
image sentiment transfer. Compared with other related tasks that have been
well-studied, such as image-to-image translation and image style transfer,
transferring the sentiment of an image is more challenging. Given an input
image, the rule to transfer the sentiment of each contained object can be
completely different, making existing approaches that perform global image
transfer by a single reference image inadequate to achieve satisfactory
performance. In this paper, we propose an effective and flexible framework that
performs image sentiment transfer at the object level. It first detects the
objects and extracts their pixel-level masks, and then performs object-level
sentiment transfer guided by multiple reference images for the corresponding
objects. For the core object-level sentiment transfer, we propose a novel
Sentiment-aware GAN (SentiGAN). Both global image-level and local object-level
supervisions are imposed to train SentiGAN. More importantly, an effective
content disentanglement loss cooperating with a content alignment step is
applied to better disentangle the residual sentiment-related information of the
input image. Extensive quantitative and qualitative experiments are performed
on the object-oriented VSO dataset we create, demonstrating the effectiveness
of the proposed framework.
