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Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving.
REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild
In addition to providing the signals and ground truth necessary to evaluate a wide range of speaking status detection methods, the availability of audio in REWIND makes it suitable for cross-modality studies not feasible with previous mingling datasets.
Impact of annotation modality on label quality and model performance in the automatic assessment of laughter in-the-wild
Models with video and acceleration inputs had similar performance regardless of training label modality, suggesting that it may be entirely appropriate to train models for laughter detection from body movements using video-acquired labels, despite their lower inter-rater agreement.
No-audio speaking status detection in crowded settings via visual pose-based filtering and wearable acceleration
Recognizing who is speaking in a crowded scene is a key challenge towards the understanding of the social interactions going on within.
