Vocal Bursts Valence Prediction
260 papers with code • 1 benchmarks • 1 datasets
predict the degrees of valence and arousal for the given vocal bursts
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IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art.
Generative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples.
More importantly, simply replacing the backbone with a tiny network (e. g, Xception), our Light-Head R-CNN gets 30. 7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy.
Previous attempts at RST-style discourse segmentation typically adopt features centered on a single token to predict whether to insert a boundary before that token.
Different from previous work, MagNet learns to differentiate between normal and adversarial examples by approximating the manifold of normal examples.
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.