1,720,972 research outputs found
Detecting Dangerous Behaviors of Mobile Objects in Parking Areas
No full textIn the last decade video-surveillance systems have been developed for monitoring remote environments in order to detect and prevent dangerous situations. Until few years ago, surveillance was performed entirely by human operators, who interpreted the visual information presented to them on one or more monitors
Exploiting data diversity in multi-domain federated learning
Full text linkFederated learning (FL) is an evolving machine learning technique that allows collaborative model training without sharing the original data among participants. In real-world scenarios, data residing at multiple clients are often heterogeneous in terms of different resolutions, magnifications, scanners, or imaging protocols, and thus challenging for global FL model convergence in collaborative training. Most of the existing FL methods consider data heterogeneity within one domain by assuming same data variation in each client site. In this paper, we consider data heterogeneity in FL with different domains of heterogeneous data by raising the problems of domain-shift, class-imbalance, and missing data. We propose a method, multi-domain FL as a solution to heterogeneous training data from multiple domains by training robust vision transformer model. We use two loss functions, one for correctly predicting class labels and other for encouraging similarity and dissimilarity over latent features, to optimize the global FL model. We perform various experiments using different convolution-based networks and non-convolutional Transformer architectures on multi-domain datasets. We evaluate the proposed approach on benchmark datasets and compare with the existing FL methods. Our results show the superiority of the proposed approach which performs better in term of robust FL global model than the exiting methods
A late fusion deep neural network for robust speaker identification using raw waveforms and gammatone cepstral coefficients
No full textSpeaker identification aims at determining the speaker identity by analyzing his voice characteristics, and relies typically on statistical models or machine learning techniques. Frequency-domain features are by far the most used choice to encode the audio input in sound recognition. Recently, some studies have also analyzed the use of time-domain raw waveform (RW) with deep neural network (DNN) architectures. In this paper, we hypothesize that both time-domain and frequency-domain features can be used to increase the robustness of speaker identification task in adverse noisy and reverberation conditions, and we present a method based on a late fusion DNN using RWs and gammatone cepstral coefficients (GTCCs). We analyze the characteristics of RW and spectrum-based short-time features, reporting advantages and limitations, and we show that the joint use can increase the identification accuracy. The proposed late fusion DNN model consists of two independent DNN branches made primarily by convolutional neural networks (CNN) and fully connected neural networks (NN) layers. The two DNN branches have as input short-time RW audio fragments and GTCCs, respectively. The late fusion is computed on the predicted scores of the DNN branches. Since the method is based on short segments, it has the advantage of being independent from the size of the input audio signal, and the identification task can be computed by summing the predicted scores over several short-time frames. Analysis of speaker identification performance computed with simulations show that the late fusion DNN model improves the accuracy rate in adverse noise and reverberation conditions in comparison to the RW, the GTCC, and the mel-frequency cepstral coefficients (MFCCs) features. Experiments with real-world speech datasets confirm the efficiency of the proposed method, especially with small-size audio samples
New Error Measures To Evaluate Features on Three Dimensional Scenes
No full textIn this paper new error measures to evaluate image features in three-dimensional scenes are proposed and reviewed. The proposed error measures are designed to take into account feature shapes, and ground truth data can be easily estimated. As other approaches, they are not error-free and a quantitative evaluation is given according to the number of wrong matches and mismatches in order to assess their validit
Impairments in Decoding Facial and Vocal Emotional Expressions in High Functioning Autistic Adults and Adolescents.
Full text link- …
