381 research outputs found
Dynamic Face Models: Construction and Applications
A thesis submitted to the University of London for the degree of Doctor of Philosoph
Video Classification Using Spatial-Temporal Features and PCA
We investigate the problem of automated video classification by analysing the low-level audio-visual signal patterns along the time course in a holistic manner. Five popular TV broadcast genre are studied including sports, cartoon, news, commercial and music. A novel statistically based approach is proposed comprising two important ingredients designed for implicit semantic content characterisation and class identities modelling. First, a spatial-temporal audio-visual "super" feature vector is computed, capturing crucial clip-level video structure information inherent in a video genre. Second, the feature vector is further processed using Principal Component Analysis to reduce the spatial-temporal redundancy while exploiting the correlations between feature elements, which give rise to a compact representation for effective probabilistic modelling of each video genre. Extensive experiments are conducted assessing various aspects of the approach and their influence on the overall system performance
Performance Analysis of UNet and Variants for Medical Image Segmentation
Medical imaging plays a crucial role in modern healthcare by providing
non-invasive visualisation of internal structures and abnormalities, enabling
early disease detection, accurate diagnosis, and treatment planning. This study
aims to explore the application of deep learning models, particularly focusing
on the UNet architecture and its variants, in medical image segmentation. We
seek to evaluate the performance of these models across various challenging
medical image segmentation tasks, addressing issues such as image
normalization, resizing, architecture choices, loss function design, and
hyperparameter tuning. The findings reveal that the standard UNet, when
extended with a deep network layer, is a proficient medical image segmentation
model, while the Res-UNet and Attention Res-UNet architectures demonstrate
smoother convergence and superior performance, particularly when handling fine
image details. The study also addresses the challenge of high class imbalance
through careful preprocessing and loss function definitions. We anticipate that
the results of this study will provide useful insights for researchers seeking
to apply these models to new medical imaging problems and offer guidance and
best practices for their implementation
Experience Goods and Consumer Search
We introduce a search model where products differ in variety and unobserved quality (`experience goods'), and firms can establish quality reputation. We show that the inability of consumers to observe quality before purchase significantly changes how search frictions affect market performance. In equilibrium, higher search costs hinder consumers' search for better-matched variety and increase price, but can boost firms' investment in product quality. Under plausible conditions, both consumer and total welfare initially increase in search cost, whereas both would monotonically decrease if quality were observable. We apply the analysis to online markets, where low search costs coexist with low-quality products
Experience Goods and Consumer Search
We introduce a search model where products differ in variety and unobserved quality (`experience goods'), and firms can establish quality reputation. We show that the inability of consumers to observe quality before purchase significantly changes how search frictions affect market performance. In equilibrium, higher search costs hinder consumers' search for better-matched variety and increase price, but can boost firms' investment in product quality. Under plausible conditions, both consumer and total welfare initially increase in search cost, whereas both would monotonically decrease if quality were observable. We apply the analysis to online markets, where low search costs coexist with low-quality products
Towards Enhancing In-Context Learning for Code Generation
In-context learning (ICL) with pre-trained language models (PTLMs) has shown
great success in code generation. ICL does not require training. PTLMs take as
the input a prompt consisting of a few requirement-code examples and a new
requirement, and output a new program. However, existing studies simply reuse
ICL techniques for natural language generation and ignore unique features of
code generation. We refer to these studies as standard ICL.
Inspired by observations of the human coding process, we propose a novel ICL
approach for code generation named AceCoder. Compared to standard ICL, AceCoder
has two novelties. (1) Example retrieval. It retrieves similar programs as
examples and learns programming skills (e.g., algorithms, APIs) from them. (2)
Guided Code Generation. It encourages PTLMs to output an intermediate
preliminary (e.g., test cases, APIs) before generating programs. The
preliminary can help PTLMs understand requirements and guide the next code
generation. We apply AceCoder to six PTLMs (e.g., Codex) and evaluate it on
three public benchmarks using the Pass@k. Results show that AceCoder can
significantly improve the performance of PTLMs on code generation. (1) In terms
of Pass@1, AceCoder outperforms standard ICL by up to 79.7% and fine-tuned
models by up to 171%. (2) AceCoder is effective in PTLMs with different sizes
(e.g., 1B to 175B) and different languages (e.g., Python, Java, and
JavaScript). (3) We investigate multiple choices of the intermediate
preliminary. (4) We manually evaluate generated programs in three aspects and
prove the superiority of AceCoder. (5) Finally, we discuss some insights about
ICL for practitioners
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