Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 70 for “"feature representation"”.
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Facial feature representation and recognition
… and social interaction. Facial expression representation and recognition have become a promising research area during recent years. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this dissertation, the fundamental techniques will be …
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A linguistic feature representation of the speech waveform
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
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SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
… learning do not fully support some important features for big data analytics such as incremental learning, distributed learning, and fuzzy matching. In this thesis, we propose a unique feature representation, named the SigSpace. It is designed for a class-level incremental learning in support …
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Complex Acoustical Feature Representation in the Zebra Finch Caudomedial Neostriatum (Ncm)
… thesis, we present a primary analysis of the representational strategies available in the neurophysiological responses of neurons in the NCM. We developed a pair of matched auditory stimuli that share the same acoustic envelope (acoustic contour), but differ in their spectral organization …
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Enhanced Feature Representation in Multi-Modal Learning for Driving Safety Assessment
This dissertation explores innovative approaches in driving safety through the development of multi-modal learning frameworks that leverage high-frequency, high-resolution driving data and videos to detect safety-critical events (SCEs). The research unfolds across four methodologies, each …
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A hierarchical feature representation for phonetic classification dc by Raymond Y.T. Chun.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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Action Intention Modulates the Temporal Dynamics of Object Feature Representation: An Electrophysiological Approach
… shown to influence the computation of visual features in a task-relevant manner. Ideomotor theories would suggest that the intention to act on an object is sufficient to integrate its task-relevant visual features, however, support for these theories draw from paradigms that arbitrarily …
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Cue competition and feature representation in a category learning task: an fMRI study
… is limited, and therefore selecting what feature(s) to attend to in the environment is important. Sometimes, attention is captured by a cue or feature in such a way that other cues or features are not attended to, known as overshadowing. This process is not entirely understood in category …
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Road sign recognition
… segmentation. We then designed a hierarchical feature representation scheme, which organizes model features in a tree structure. The tree nodes are components that form the basic parts of a model, and the tree leaves are primitive physical features that can be mapped directly to basic features …
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Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation
… (1) for the stuff categories, we generate the feature representation for each class and conduct the alignment operation from the target domain to the source domain; (2) for the thing categories, we generate the feature representation for each individual instance and encourage the instance in …
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Image and video object selection
… most of them use some low-level, handcrafted features which are not optimal. Second, they also lack the high-level understanding of ""objectness"" and semantics. Last but not the least, their generalization ability on unconstrained scenarios is very poor. Recently, deep learning has become the …
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A uniform representation for visual concepts
… describing them. Our method uses a uniform feature representation for all words and word types rather than relying on handcrafted features specific to each word. We learn words in a weakly-supervised manner, with no need for annotated bounding boxes around objects of interest. We encode …
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Conditional Neural Networks for Speech and Language Processing
… and consideration domain expertise to design a feature extractor that transformed the raw data to a suitable internal representation. Its extreme efficacy on multiple levels of representation and feature learning ensures this type of approaches can process high dimensional data. It integrates …
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An integrated approach for multimedia information retrieval.
… retrieval has been developed in which multiple feature representation and matching schemes can be incorporated. At the theoretical level this framework integrates multiple retrieval techniques, and the Dempster-Shafer theory of evidence combination is employed for this purpose. The integration …
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Application of Prior Information to Discriminative Feature Learning
Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image …
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Bayesian models for visual information retrieval
… careful consideration of the interplay between feature selection, feature representation, and similarity function, we start by searching for a performance criteria that can simultaneously guide the design of all three components. A natural solution is to formulate visual recognition as a …
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Attention-enhanced Cross-View Transformer for monocular BEV perception
… Block Attention Module (CBAM) to improve feature representation. Our method incorporates spatial-channel attention to refine encoder features, followed by Cycled View Projection (CVP) and a Cross-View Transformer (CVT) for view transformation. CVP enforces geometric coherence through …
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Creating music by listening
… musical surface, which leads to a minimal data representation. We introduce a music cognition framework that results from the interaction of psychoacoustically grounded causal listening, a time-lag embedded feature representation, and perceptual similarity clustering. Our bottom-up analysis …
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A Large Collection Learning Optimizer Framework
… syntactic relationships using state of the art feature selection techniques like Word2Vec. Machine learning techniques, using word features that capture semantic and context relationships, can be of benefit regarding classification accuracy. Improving text classification results on Twitter data …
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Dynamically Instance-Guided Adaptation: A Backward-free Approach for Test-Time Domain Adaptive Semantic Segmentation
… Batch Normalization (BN) statistics for robust feature representation, and a Semantic Adaptation Module (SAM) which constructs a dynamic non-parametric classifier using historical and instance-aware prototypes to refine semantic predictions. Evaluated on five diverse benchmark datasets, DIGA …
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