Residual Learning for FC Kernels of Convolutional Network

Alexeev, Alexey; Matveev, Yuri; Matveev, Anton; Pavlenko, Dmitry

doi:10.1007/978-3-030-30484-3_30

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, when a raw image is processed through a fully connected neural network, the network has to treat each pixel as an individual input and learn to extract relevant features from all locations within the image. In contrast, a convolutional neural network (CNN) [45] can learn to recognize patterns in an image regardless of where they are located, using shared weights across the entire image and reducing the number of parameters required. By design, CNNs learn hierarchical representations of the raw input data and, due to the shown efficiency of this approach, this is the most common approach for the representation of visual data.…”

Section: Audio-visual Emotion Recognitionmentioning

confidence: 99%

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

Matveev,

Frolova

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

Detecting and understanding emotions are critical for our daily activities. As emotion recognition (ER) systems develop, we start looking at more difficult cases than just acted adult audio–visual speech. In this work, we investigate the automatic classification of the audio–visual emotional speech of children, which presents several challenges including the lack of publicly available annotated datasets and the low performance of the state-of-the art audio–visual ER systems. In this paper, we present a new corpus of children’s audio–visual emotional speech that we collected. Then, we propose a neural network solution that improves the utilization of the temporal relationships between audio and video modalities in the cross-modal fusion for children’s audio–visual emotion recognition. We select a state-of-the-art neural network architecture as a baseline and present several modifications focused on a deeper learning of the cross-modal temporal relationships using attention. By conducting experiments with our proposed approach and the selected baseline model, we observe a relative improvement in performance by 2%. Finally, we conclude that focusing more on the cross-modal temporal relationships may be beneficial for building ER systems for child–machine communications and environments where qualified professionals work with children.

show abstract

Section: Audio-visual Emotion Recognitionmentioning

confidence: 99%

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

Matveev,

Frolova

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The suggested architecture contains non-linear classifiers type NiN [23][24][25], more specifically the solution a3net [26], which follows a particular approach to identitying matrix initialization for building a deep network [27]. The initialization of the identity matrix occurs at the start of the process with the weight (including the weights for the unitary diagonal) changing during the training process.…”

Section: Amr Detectormentioning

confidence: 99%

“…The main diagonal is initialized as w 1ii = λ for each i, where λ -> 1, as in [27]. This allows building efficiently trained deep neural network architectures (see also Section 2.1).…”

Section: Characteristics Of the Networkmentioning

confidence: 99%

A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions

Alexeev¹,

Kukharev

Matveev

et al. 2020

Mathematics

Self Cite

View full text Add to dashboard Cite

We investigate a neural network–based solution for the Automatic Meter Reading detection problem, applied to analog dial gauges. We employ a convolutional neural network with a non-linear Network in Network kernel. Presently, there is a significant interest in systems for automatic detection of analog dial gauges, particularly in the energy and household sectors, but the problem is not yet sufficiently addressed in research. Our method is a universal three-level model that takes an image as an input and outputs circular bounding areas, object classes, grids of reference points for all symbols on the front panel of the device and positions of display pointers. Since all analog pointer meters have a common nature, this multi-cascade model can serve various types of devices if its capacity is sufficient. The model is using global regression for locations of symbols, which provides resilient results even for low image quality and overlapping symbols. In this work, we do not focus on the pointer location detection since it heavily depends on the shape of the pointer. We prepare training data and benchmark the algorithm with our own framework a3net, not relying on third-party neural network solutions. The experimental results demonstrate the versatility of the proposed methods, high accuracy, and resilience of reference points detection.

show abstract

A Neural Network Architecture for Children’s Audio-Visual Emotion Recognition

Matveev,

Frolova

et al. 2023

Preprint

View full text Add to dashboard Cite

Detecting and understanding emotions is critical for our daily activities. As emotion recognition (ER) systems develop, we start looking at more difficult cases than just acted adult audio-visual speech. In this work, we investigate automatic classification of audio-visual emotional speech of children. Our interest is, specifically, in the improvement of the utilization of the cross-modal relationships between the selected modalities: video and audio. To underscore the importance of developing ER systems for the real-world environment, we present a corpus of children’s emotional audio-visual speech that we collected. We select a state-of-the-art model as a baseline for the purposes of comparison and present several modifications focused on a deeper learning of the cross-modal relationships. By conducting experiments with our proposed approach and the selected baseline model, we observe a relative improvement in performance by 2%. Finally, we conclude that focusing more on the cross-modal relationships may be beneficial for building ER systems for child-machine communications and the environments where qualified professionals work with children.

show abstract

Residual Learning for FC Kernels of Convolutional Network

Cited by 3 publications

References 8 publications

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition

A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions

A Neural Network Architecture for Children’s Audio-Visual Emotion Recognition

Contact Info

Product

Resources

About