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Weakly-supervised Caricature Face Parsing through Domain Adaptation
A caricature is an artistic form of a person's picture in which certain
striking characteristics are abstracted or exaggerated in order to create a
humor or sarcasm effect. For numerous caricature related applications such as
attribute recognition and caricature editing, face parsing is an essential
pre-processing step that provides a complete facial structure understanding.
However, current state-of-the-art face parsing methods require large amounts of
labeled data on the pixel-level and such process for caricature is tedious and
labor-intensive. For real photos, there are numerous labeled datasets for face
parsing. Thus, we formulate caricature face parsing as a domain adaptation
problem, where real photos play the role of the source domain, adapting to the
target caricatures. Specifically, we first leverage a spatial transformer based
network to enable shape domain shifts. A feed-forward style transfer network is
then utilized to capture texture-level domain gaps. With these two steps, we
synthesize face caricatures from real photos, and thus we can use parsing
ground truths of the original photos to learn the parsing model. Experimental
results on the synthetic and real caricatures demonstrate the effectiveness of
the proposed domain adaptation algorithm. Code is available at:
https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at
https://github.com/ZJULearning/CariFaceParsin
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An improved hidden vector state model approach and its adaptation in extracting protein interaction information from biomedical literature
Large quantity of knowledge, which is important for biological researchers to unveil the mechanism of life, often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and full parsing, have been proposed especially for biomedical applications. In this paper, we present an information extraction system employing a semantic parser using the Hidden Vector State (HVS) model for protein-protein interactions. We found that it performed better than other established statistical methods and achieved 58.3% and 76.8% in recall and precision respectively. Moreover, the pure data-driven HVS model can be easily adapted to other domains, which is rarely mentioned and possessed by other approaches. Experimental results prove that the model trained on one domain can still generate satisfactory results when shifting to another domain with a small amount of adaptation training data
Robustness issues in a data-driven spoken language understanding system
Robustness is a key requirement in spoken language understanding (SLU) systems. Human speech is often ungrammatical and ill-formed, and there will frequently be a mismatch between training and test data. This paper discusses robustness and adaptation issues in a statistically-based SLU system which is entirely data-driven. To test robustness, the system has been tested on data from the Air Travel Information Service (ATIS) domain which has been artificially corrupted with varying levels of additive noise. Although the speech recognition performance degraded steadily, the system did not fail catastrophically. Indeed, the rate at which the end-to-end performance of the complete system degraded was significantly slower than that of the actual recognition component. In a second set of experiments, the ability to rapidly adapt the core understanding component of the system to a different application within the same broad domain has been tested. Using only a small amount of training data, experiments have shown that a semantic parser based on the Hidden Vector State (HVS) model originally trained on the ATIS corpus can be straightforwardly adapted to the somewhat different DARPA Communicator task using standard adaptation algorithms. The paper concludes by suggesting that the results presented provide initial support to the claim that an SLU system which is statistically-based and trained entirely from data is intrinsically robust and can be readily adapted to new applications
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