259 research outputs found
Interpretable global-local dynamics for the prediction of eye fixations in autonomous driving scenarios
Human eye movements while driving reveal that visual attention largely depends on the context in which it occurs. Furthermore, an autonomous vehicle which performs this function would be more reliable if its outputs were understandable. Capsule Networks have been presented as a great opportunity to explore new horizons in the Computer Vision field, due to their capability to structure and relate latent information. In this article, we present a hierarchical approach for the prediction of eye fixations in autonomous driving scenarios. Context-driven visual attention can be modeled by considering different conditions which, in turn, are represented as combinations of several spatio-temporal features. With the aim of learning these conditions, we have built an encoder-decoder network which merges visual features' information using a global-local definition of capsules. Two types of capsules are distinguished: representational capsules for features and discriminative capsules for conditions. The latter and the use of eye fixations recorded with wearable eye tracking glasses allow the model to learn both to predict contextual conditions and to estimate visual attention, by means of a multi-task loss function. Experiments show how our approach is able to express either frame-level (global) or pixel-wise (local) relationships between features and contextual conditions, allowing for interpretability while maintaining or improving the performance of black-box related systems in the literature. Indeed, our proposal offers an improvement of 29% in terms of information gain with respect to the best performance reported in the literature.The authors would like to thank the authors from DR(eye)VE Project [49] for the support provided during this work, as well as the Multimedia Processing Group from the Universidad Carlos III de Madrid for their entire personal and academic implication
H2CGL: Modeling Dynamics of Citation Network for Impact Prediction
The potential impact of a paper is often quantified by how many citations it
will receive. However, most commonly used models may underestimate the
influence of newly published papers over time, and fail to encapsulate this
dynamics of citation network into the graph. In this study, we construct
hierarchical and heterogeneous graphs for target papers with an annual
perspective. The constructed graphs can record the annual dynamics of target
papers' scientific context information. Then, a novel graph neural network,
Hierarchical and Heterogeneous Contrastive Graph Learning Model (H2CGL), is
proposed to incorporate heterogeneity and dynamics of the citation network.
H2CGL separately aggregates the heterogeneous information for each year and
prioritizes the highly-cited papers and relationships among references,
citations, and the target paper. It then employs a weighted GIN to capture
dynamics between heterogeneous subgraphs over years. Moreover, it leverages
contrastive learning to make the graph representations more sensitive to
potential citations. Particularly, co-cited or co-citing papers of the target
paper with large citation gap are taken as hard negative samples, while
randomly dropping low-cited papers could generate positive samples. Extensive
experimental results on two scholarly datasets demonstrate that the proposed
H2CGL significantly outperforms a series of baseline approaches for both
previously and freshly published papers. Additional analyses highlight the
significance of the proposed modules. Our codes and settings have been released
on Github (https://github.com/ECNU-Text-Computing/H2CGL)Comment: Accepted by IP&
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Digital media enables not only fast sharing of information, but also
disinformation. One prominent case of an event leading to circulation of
disinformation on social media is the MH17 plane crash. Studies analysing the
spread of information about this event on Twitter have focused on small,
manually annotated datasets, or used proxys for data annotation. In this work,
we examine to what extent text classifiers can be used to label data for
subsequent content analysis, in particular we focus on predicting pro-Russian
and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though
we find that a neural classifier improves over a hashtag based baseline,
labeling pro-Russian and pro-Ukrainian content with high precision remains a
challenging problem. We provide an error analysis underlining the difficulty of
the task and identify factors that might help improve classification in future
work. Finally, we show how the classifier can facilitate the annotation task
for human annotators
Human-Computer Interaction
In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
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