251 research outputs found
An EMA study of the articulatory-acoustic relationship of Cantonese corner vowels
"A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, June 30, 2009."Includes bibliographical references (p. 21-23).Thesis (B.Sc)--University of Hong Kong, 2009.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science
FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking
Automatic fact-checking plays a crucial role in combating the spread of
misinformation. Large Language Models (LLMs) and Instruction-Following
variants, such as InstructGPT and Alpaca, have shown remarkable performance in
various natural language processing tasks. However, their knowledge may not
always be up-to-date or sufficient, potentially leading to inaccuracies in
fact-checking. To address this limitation, we propose combining the power of
instruction-following language models with external evidence retrieval to
enhance fact-checking performance. Our approach involves leveraging search
engines to retrieve relevant evidence for a given input claim. This external
evidence serves as valuable supplementary information to augment the knowledge
of the pretrained language model. Then, we instruct-tune an open-sourced
language model, called LLaMA, using this evidence, enabling it to predict the
veracity of the input claim more accurately. To evaluate our method, we
conducted experiments on two widely used fact-checking datasets: RAWFC and
LIAR. The results demonstrate that our approach achieves state-of-the-art
performance in fact-checking tasks. By integrating external evidence, we bridge
the gap between the model's knowledge and the most up-to-date and sufficient
context available, leading to improved fact-checking outcomes. Our findings
have implications for combating misinformation and promoting the dissemination
of accurate information on online platforms. Our released materials are
accessible at: https://thcheung.github.io/factllama.Comment: Accepted in APSIPA ASC 202
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding
Self-supervised representation learning (SSRL) has gained increasing
attention in point cloud understanding, in addressing the challenges posed by
3D data scarcity and high annotation costs. This paper presents PCExpert, a
novel SSRL approach that reinterprets point clouds as "specialized images".
This conceptual shift allows PCExpert to leverage knowledge derived from
large-scale image modality in a more direct and deeper manner, via extensively
sharing the parameters with a pre-trained image encoder in a multi-way
Transformer architecture. The parameter sharing strategy, combined with a novel
pretext task for pre-training, i.e., transformation estimation, empowers
PCExpert to outperform the state of the arts in a variety of tasks, with a
remarkable reduction in the number of trainable parameters. Notably, PCExpert's
performance under LINEAR fine-tuning (e.g., yielding a 90.02% overall accuracy
on ScanObjectNN) has already approached the results obtained with FULL model
fine-tuning (92.66%), demonstrating its effective and robust representation
capability
Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis
Convolutional neural network (CNN)-based image denoising methods have been
widely studied recently, because of their high-speed processing capability and
good visual quality. However, most of the existing CNN-based denoisers learn
the image prior from the spatial domain, and suffer from the problem of
spatially variant noise, which limits their performance in real-world image
denoising tasks. In this paper, we propose a discrete wavelet denoising CNN
(WDnCNN), which restores images corrupted by various noise with a single model.
Since most of the content or energy of natural images resides in the
low-frequency spectrum, their transformed coefficients in the frequency domain
are highly imbalanced. To address this issue, we present a band normalization
module (BNM) to normalize the coefficients from different parts of the
frequency spectrum. Moreover, we employ a band discriminative training (BDT)
criterion to enhance the model regression. We evaluate the proposed WDnCNN, and
compare it with other state-of-the-art denoisers. Experimental results show
that WDnCNN achieves promising performance in both synthetic and real noise
reduction, making it a potential solution to many practical image denoising
applications.Comment: ICIP 201
Multi-scale Sampling and Aggregation Network For High Dynamic Range Imaging
High dynamic range (HDR) imaging is a fundamental problem in image
processing, which aims to generate well-exposed images, even in the presence of
varying illumination in the scenes. In recent years, multi-exposure fusion
methods have achieved remarkable results, which merge multiple low dynamic
range (LDR) images, captured with different exposures, to generate
corresponding HDR images. However, synthesizing HDR images in dynamic scenes is
still challenging and in high demand. There are two challenges in producing HDR
images: 1). Object motion between LDR images can easily cause undesirable
ghosting artifacts in the generated results. 2). Under and overexposed regions
often contain distorted image content, because of insufficient compensation for
these regions in the merging stage. In this paper, we propose a multi-scale
sampling and aggregation network for HDR imaging in dynamic scenes. To
effectively alleviate the problems caused by small and large motions, our
method implicitly aligns LDR images by sampling and aggregating
high-correspondence features in a coarse-to-fine manner. Furthermore, we
propose a densely connected network based on discrete wavelet transform for
performance improvement, which decomposes the input into several
non-overlapping frequency subbands and adaptively performs compensation in the
wavelet domain. Experiments show that our proposed method can achieve
state-of-the-art performances under diverse scenes, compared to other promising
HDR imaging methods. In addition, the HDR images generated by our method
contain cleaner and more detailed content, with fewer distortions, leading to
better visual quality
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