1,663 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
Domain-adaptive Message Passing Graph Neural Network
Cross-network node classification (CNNC), which aims to classify nodes in a
label-deficient target network by transferring the knowledge from a source
network with abundant labels, draws increasing attention recently. To address
CNNC, we propose a domain-adaptive message passing graph neural network
(DM-GNN), which integrates graph neural network (GNN) with conditional
adversarial domain adaptation. DM-GNN is capable of learning informative
representations for node classification that are also transferrable across
networks. Firstly, a GNN encoder is constructed by dual feature extractors to
separate ego-embedding learning from neighbor-embedding learning so as to
jointly capture commonality and discrimination between connected nodes.
Secondly, a label propagation node classifier is proposed to refine each node's
label prediction by combining its own prediction and its neighbors' prediction.
In addition, a label-aware propagation scheme is devised for the labeled source
network to promote intra-class propagation while avoiding inter-class
propagation, thus yielding label-discriminative source embeddings. Thirdly,
conditional adversarial domain adaptation is performed to take the
neighborhood-refined class-label information into account during adversarial
domain adaptation, so that the class-conditional distributions across networks
can be better matched. Comparisons with eleven state-of-the-art methods
demonstrate the effectiveness of the proposed DM-GNN
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