  Image captioning can automatically generate captions for the given images,
and the key challenge is to learn a mapping function from visual features to
natural language features. Existing approaches are mostly supervised ones,
i.e., each image has a corresponding sentence in the training set. However,
considering that describing images always requires a huge of manpower, we
usually have limited amount of described images (i.e., image-text pairs) and a
large number of undescribed images in real-world applications. Thereby, a
dilemma is the "Semi-Supervised Image Captioning". To solve this problem, we
propose a novel Semi-Supervised Image Captioning method considering Wasserstein
Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to
supervise the generated sentences. Different from traditional single modal
semi-supervised methods, the difficulty of semi-supervised cross-modal learning
lies in constructing intermediately comparable information among heterogeneous
modalities. In this paper, SSIC-WGM adopts the successful scene graphs as
intermediate information, and constrains the generated sentences from two
aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of
the raw image and generated sentence respectively, then employs the wasserstein
distance to better measure the similarity between region embeddings of
different graphs. 2) intra-modal consistency. SSIC-WGM takes the data
augmentation techniques for the raw images, then constrains the consistency
among augmented images and generated sentences. Consequently, SSIC-WGM combines
the cross-modal pseudo supervision and structure invariant measure for
efficiently using the undescribed images, and learns more reasonable mapping
function.
