Initiation of Interaction Detection Framework using a Nonverbal Cue for Human-Robot Interaction

This paper describes an initiation of interaction(IoI) detection framework without keywords for human-robot interaction(HRI) based on audio and vision sensor fusion in a domestic environment. In the proposed framework, the robot has its own audio and vision sensors, and can employ external vision sensor for stable human detection and tracking. When the user starts to speak while looking at the robot, the robot can localize his or her position by its sound source localization together with human tracking information. Then the robot can detect the IoI if it perceives the face of the speaker faces the robot. In case that the user does not speak directly, the robot can also detect the IoI if he or she looks at the robot for more than predefined periods of time. A state transition model for the proposed IoI detection framework is designed and verified by experiments with a mobile robot. In order to implement and associate our model in a robot architecture, all the components are implemented and integrated in the Robot Operating System(ROS) environment.

Spectral-Adaptive Modulation Networks for Visual Perception

Recent studies have shown that 2D convolution and self-attention exhibit distinct spectral behaviors, and optimizing their spectral properties can enhance vision model performance. However, theoretical analyses remain limited in explaining why 2D convolution is more effective in high-pass filtering than self-attention and why larger kernels favor shape bias, akin to self-attention. In this paper, we employ graph spectral analysis to theoretically simulate and compare the frequency responses of 2D convolution and self-attention within a unified framework. Our results corroborate previous empirical findings and reveal that node connectivity, modulated by window size, is a key factor in shaping spectral functions. Leveraging this insight, we introduce a \textit{spectral-adaptive modulation} (SPAM) mixer, which processes visual features in a spectral-adaptive manner using multi-scale convolutional kernels and a spectral re-scaling mechanism to refine spectral components. Based on SPAM, we develop SPANetV2 as a novel vision backbone. Extensive experiments demonstrate that SPANetV2 outperforms state-of-the-art models across multiple vision tasks, including ImageNet-1K classification, COCO object detection, and ADE20K semantic segmentation.

SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation

Recent studies show that self-attentions behave like low-pass filters (as opposed to convolutions) and enhancing their high-pass filtering capability improves model performance. Contrary to this idea, we investigate existing convolution-based models with spectral analysis and observe that improving the low-pass filtering in convolution operations also leads to performance improvement. To account for this observation, we hypothesize that utilizing optimal token mixers that capture balanced representations of both high- and low-frequency components can enhance the performance of models. We verify this by decomposing visual features into the frequency domain and combining them in a balanced manner. To handle this, we replace the balancing problem with a mask filtering problem in the frequency domain. Then, we introduce a novel token-mixer named SPAM and leverage it to derive a MetaFormer model termed as SPANet. Experimental results show that the proposed method provides a way to achieve this balance, and the balanced representations of both high- and low-frequency components can improve the performance of models on multiple computer vision tasks. Our code is available at $\href{https://doranlyong.github.io/projects/spanet/}{\text{https://doranlyong.github.io/projects/spanet/}}$.