13,123 research outputs found
Auto-Encoding Scene Graphs for Image Captioning
We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language
inductive bias into the encoder-decoder image captioning framework for more
human-like captions. Intuitively, we humans use the inductive bias to compose
collocations and contextual inference in discourse. For example, when we see
the relation `person on bike', it is natural to replace `on' with `ride' and
infer `person riding bike on a road' even the `road' is not evident. Therefore,
exploiting such bias as a language prior is expected to help the conventional
encoder-decoder models less likely overfit to the dataset bias and focus on
reasoning. Specifically, we use the scene graph --- a directed graph
() where an object node is connected by adjective nodes and
relationship nodes --- to represent the complex structural layout of both image
() and sentence (). In the textual domain, we use
SGAE to learn a dictionary () that helps to reconstruct sentences
in the pipeline, where encodes the desired language prior;
in the vision-language domain, we use the shared to guide the
encoder-decoder in the pipeline. Thanks to the scene graph
representation and shared dictionary, the inductive bias is transferred across
domains in principle. We validate the effectiveness of SGAE on the challenging
MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves
a new state-of-the-art CIDEr-D on the Karpathy split, and a competitive
CIDEr-D (c40) on the official server even compared to other ensemble
models
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
In this paper, we propose GPSP, a novel Graph Partition and Space Projection
based approach, to learn the representation of a heterogeneous network that
consists of multiple types of nodes and links. Concretely, we first partition
the heterogeneous network into homogeneous and bipartite subnetworks. Then, the
projective relations hidden in bipartite subnetworks are extracted by learning
the projective embedding vectors. Finally, we concatenate the projective
vectors from bipartite subnetworks with the ones learned from homogeneous
subnetworks to form the final representation of the heterogeneous network.
Extensive experiments are conducted on a real-life dataset. The results
demonstrate that GPSP outperforms the state-of-the-art baselines in two key
network mining tasks: node classification and clustering.Comment: WWW 2018 Poste
- β¦