An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning

This paper illustrates our submission method to the fourth Affective Behavior Analysis in-the-Wild (ABAW) Competition. The method is used for the Multi-Task Learning Challenge. Instead of using only face information, we employ full information from a provided dataset containing face and the context around the face. We utilized the InceptionNet V3 model to extract deep features then we applied the attention mechanism to refine the features. After that, we put those features into the transformer block and multi-layer perceptron networks to get the final multiple kinds of emotion. Our model predicts arousal and valence, classifies the emotional expression and estimates the action units simultaneously. The proposed system achieves the performance of 0.917 on the MTL Challenge validation dataset

Temporal Convolution Networks with Positional Encoding for Evoked Expression Estimation

This paper presents an approach for Evoked Expressions from Videos (EEV) challenge, which aims to predict evoked facial expressions from video. We take advantage of pre-trained models on large-scale datasets in computer vision and audio signals to extract the deep representation of timestamps in the video. A temporal convolution network, rather than an RNN like architecture, is used to explore temporal relationships due to its advantage in memory consumption and parallelism. Furthermore, to address the missing annotations of some timestamps, positional encoding is employed to ensure continuity of input data when discarding these timestamps during training. We achieved state-of-the-art results on the EEV challenge with a Pearson correlation coefficient of 0.05477, the first ranked performance in the EEV 2021 challenge.Comment: Oral presentation at AUVi Workshop - CVPR 2021 (https://sites.google.com/view/auvi-cvpr2021/program). Source code available at https://github.com/th2l/EvokedExpression-tcnp

Quasi-Eigenstate Evolution in Open Chaotic Billiards

We experimentally studied evolution of quasi-eigenmodes as classical dynamics undergoing a transition from being regular to chaotic in open quantum billiards. In a deformation-variable microcavity we traced all high-Q cavity modes in a wide range of frequency as the cavity deformation increased. By employing an internal parameter we were able to obtain a mode-dynamics diagram at a given deformation, showing avoided crossings between different mode groups, and could directly observe the coupling strengths induced by ray chaos among encountering modes. We also show that the observed mode-dynamics diagrams reflect the underlying classical ray dynamics in the phase space.Comment: 4 pages, 4 figure

Development of deformation-tunable quadrupolar microcavity

We have developed a technique for realizing a two-dimensional quadrupolar microcavity with its deformation variable from 0% to 20% continuously. We employed a microjet ejected from a noncircular orifice in order to generate a stationary column with modulated quadrupolar deformation in its cross section. Wavelength red shifts of low-order cavity modes due to shape deformation were measured and were found to be in good agreement with the wave calculation for the same deformation, indicating the observed deformation is quadrupolar in nature.Comment: 7 pages, 6 figures, intended for Rev. Sci. Instu

Chaos-assisted nonresonant optical pumping of quadrupole-deformed microlasers

Efficient nonresonant optical pumping of a high-Q scar mode in a two-dimensional quadrupole-deformed microlaser has been demonstrated based on ray and wave chaos. Three-fold enhancement in the lasing power was achieved at a properly chosen pumping angle. The experimental result is consistent with ray tracing and wave overlap integral calculations.Comment: 3 pages, 5 figure