449 research outputs found
NDF: Neural Deformable Fields for Dynamic Human Modelling
We propose Neural Deformable Fields (NDF), a new representation for dynamic
human digitization from a multi-view video. Recent works proposed to represent
a dynamic human body with shared canonical neural radiance fields which links
to the observation space with deformation fields estimations. However, the
learned canonical representation is static and the current design of the
deformation fields is not able to represent large movements or detailed
geometry changes. In this paper, we propose to learn a neural deformable field
wrapped around a fitted parametric body model to represent the dynamic human.
The NDF is spatially aligned by the underlying reference surface. A neural
network is then learned to map pose to the dynamics of NDF. The proposed NDF
representation can synthesize the digitized performer with novel views and
novel poses with a detailed and reasonable dynamic appearance. Experiments show
that our method significantly outperforms recent human synthesis methods.Comment: 16 pages, 7 figures. Accepted by ECCV 202
IMPROVED DESIGN OF DTW AND GMM CASCADED ARABIC SPEAKER
In this paper, we discuss about the design, implementation and assessment of a two-stage Arabic speaker recognition system, which aims to recognize a target Arabic speaker among several people. The first stage uses improved DTW (Dynamic Time Warping) algorithm and the second stage uses SA-KM-based GMM (Gaussian Mixture Model). MFCC (Mel Frequency Cepstral Coefficients) and its differences form, as acoustic feature, are extracted from the sample speeches. DTW provides three most possible speakers and then the recognition results are conveyed to GMM training processes. A specified similarity assessment algorithm, KL distance, is applied to find the best match with the target speaker. Experimental results show that text-independent recognition rate of the cascaded system reaches 90 percent
Statistical Inference with Stochastic Gradient Methods under -mixing Data
Stochastic gradient descent (SGD) is a scalable and memory-efficient
optimization algorithm for large datasets and stream data, which has drawn a
great deal of attention and popularity. The applications of SGD-based
estimators to statistical inference such as interval estimation have also
achieved great success. However, most of the related works are based on i.i.d.
observations or Markov chains. When the observations come from a mixing time
series, how to conduct valid statistical inference remains unexplored. As a
matter of fact, the general correlation among observations imposes a challenge
on interval estimation. Most existing methods may ignore this correlation and
lead to invalid confidence intervals. In this paper, we propose a mini-batch
SGD estimator for statistical inference when the data is -mixing. The
confidence intervals are constructed using an associated mini-batch bootstrap
SGD procedure. Using ``independent block'' trick from \cite{yu1994rates}, we
show that the proposed estimator is asymptotically normal, and its limiting
distribution can be effectively approximated by the bootstrap procedure. The
proposed method is memory-efficient and easy to implement in practice.
Simulation studies on synthetic data and an application to a real-world dataset
confirm our theory
Automatic keyphrase extraction on Amazon reviews
People are facing severe challenges posed by big data. As an important type of the online text, product reviews have evoked much research interest because of their commercial potential. This thesis takes Amazon camera reviews as the research focus and implements an automatic keyphrase extraction system. The system consists of three modules, including the Crawler module, the Extraction module, and the Web module. The Crawler module is responsible for capturing Amazon product reviews. The Web module is responsible for obtaining user input and displaying the final results. The Extraction module is the core processing module of the system, which analyzes product reviews according to the following sequence: (1) Pre-processing of review data, including removal of stop words and segmentation. ( 2) Candidate keyphrase extraction. Through the Spacy part-of speech tagger and Dependency parser, the dependency relationships of each review sentence are obtained, and then the feature and opinion words are extracted based on several predefined dependency rules. (3) Candidate keyphrase clustering. By using a Latent Dirichlet Allocation (LDA) model, the candidate keyphrases are clustered according to their topics . ( 4) Candidate keyphrase ranking. Two different algorithms, LDA-TFIDF and LDA-MT, are applied to rank the keyphrases in different clusters to get the representative keyphrases. The experimental results show that the system performs well in the task of keyphrase extraction
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
In this work, we introduce the concept of complex text style transfer tasks,
and constructed complex text datasets based on two widely applicable scenarios.
Our dataset is the first large-scale data set of its kind, with 700 rephrased
sentences and 1,000 sentences from the game Genshin Impact. While large
language models (LLM) have shown promise in complex text style transfer, they
have drawbacks such as data privacy concerns, network instability, and high
deployment costs. To address these issues, we explore the effectiveness of
small models (less than T5-3B) with implicit style pre-training through
contrastive learning. We also propose a method for automated evaluation of text
generation quality based on alignment with human evaluations using ChatGPT.
Finally, we compare our approach with existing methods and show that our model
achieves state-of-art performances of few-shot text style transfer models
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