- Read more about Residual Squeeze-and-Excitation U-shaped Network for Minutia Extraction in Contactless Fingerprint Images
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Slides.pdf
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- Read more about Network Topology Inference from Gaussian and Stationary Graph Signals
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Graphs have become pervasive tools to represent information and datasets with irregular support. However, in many cases, the underlying graph is either unavailable or naively obtained, calling for more advanced methods for its estimation. Indeed, graph topology inference methods that estimate the network structure from a set of signal observations have a long and well-established history. By assuming that the observations are both Gaussian and stationary in the sought graph, this paper proposes a new scheme to learn the network from nodal observations.
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- Read more about MaskDUL: Data Uncertainty Learning in Masked Face Recognition
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Since mask occlusion causes plentiful loss of facial feature, Masked Face Recognition (MFR) is a challenging image processing task, and the recognition results are susceptible to noise. However, existing MFR methods are mostly deterministic point embedding models, which are limited in representing noise images. Moreover, Data Uncertainty Learning (DUL) fails to achieve reasonable performance in MFR.
Poster.pdf
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- Read more about Delay-aware Backpressure Routing Using Graph Neural Networks
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We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to improve this delay performance is to favor shorter paths by incorporating pre-defined biases in the BP computation, such as a bias based on the shortest path (hop) distance to the destination.
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This paper investigates negative sampling for contrastive learning in the context of audio-text retrieval. The strategy for negative sampling refers to selecting negatives (either audio clips or textual descriptions) from a pool of candidates for a positive audio-text pair. We explore sampling strategies via model-estimated within-modality and cross-modality relevance scores for audio and text samples. With a constant training setting on the retrieval system from [1], we study eight sampling strategies, including hard and semi-hard negative sampling.
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- Read more about Cross-Lingual Transfer Learning for Alzheimer’s Detection from Spontaneous Speech
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Alzheimer’s disease (AD) is a progressive neurodegenerative disease most often associated with memory deficits and cognitive decline. With the aging population, there has been much interest in automated methods for cognitive impairment detection. One approach that has attracted attention in recent years is AD detection through spontaneous speech. While the results are promising, it is not certain whether the learned speech features can be generalized across languages. To fill this gap, the ADReSS-M challenge was organized.
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- Read more about IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices
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- Read more about Transient Dictionary Learning for Compressed Time-of-Flight Imaging
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Time-of-Flight imaging aims to retrieve the 3D geometry of a scene from the delay that a modulated light waveform experiences when interacting with the former.
Multi-path interference, arising from translucent objects or concave geometries, poses a challenge when the problem is to be solved from few measurements.
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