874 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
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 201
Deep calibration of the quadratic rough Heston model
The quadratic rough Heston model provides a natural way to encode Zumbach
effect in the rough volatility paradigm. We apply multi-factor approximation
and use deep learning methods to build an efficient calibration procedure for
this model. We show that the model is able to reproduce very well both SPX and
VIX implied volatilities. We typically obtain VIX option prices within the
bid-ask spread and an excellent fit of the SPX at-the-money skew. Moreover, we
also explain how to use the trained neural networks for hedging with
instantaneous computation of hedging quantities
Towards systematic intraday news screening: a liquidity-focused approach
News can convey bearish or bullish views on financial assets. Institutional
investors need to evaluate automatically the implied news sentiment based on
textual data. Given the huge amount of news articles published each day, most
of which are neutral, we present a systematic news screening method to identify
the ``true'' impactful ones, aiming for more effective development of news
sentiment learning methods. Based on several liquidity-driven variables,
including volatility, turnover, bid-ask spread, and book size, we associate
each 5-min time bin to one of two specific liquidity modes. One represents the
``calm'' state at which the market stays for most of the time and the other,
featured with relatively higher levels of volatility and trading volume,
describes the regime driven by some exogenous events. Then we focus on the
moments where the liquidity mode switches from the former to the latter and
consider the news articles published nearby impactful. We apply naive Bayes on
these filtered samples for news sentiment classification as an illustrative
example. We show that the screened dataset leads to more effective feature
capturing and thus superior performance on short-term asset return prediction
compared to the original dataset
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