Youngki Lee

Hyunseok Oh, Juheon Yi, Youngki Lee

Transformer-based models recently reached state-of-the-art single-channel speech separation accuracy; However, their extreme computational load makes it difficult to deploy them in resource-constrained mobile or IoT devices. We thus present Papez, a lightweight and computation-efficient single-channel speech separation model. Papez is based on three key techniques. We first replace the inter-chunk Transformer with small-sized auditory working memory. Second, we adaptively prune the input tokens that do not need further processing. Finally, we reduce the number of parameters through the recurrent transformer. Our extensive evaluation shows that Papez achieves the best resource and accuracy tradeoffs with a large margin. We publicly share our source code at \texttt{https://github.com/snuhcs/Papez}

Changmin Jeon, Seonjun Kim, Juheon Yi et al.

In this paper, we present Mondrian, an edge system that enables high-performance object detection on high-resolution video streams. Many lightweight models and system optimization techniques have been proposed for resource-constrained devices, but they do not fully utilize the potential of the accelerators over dynamic, high-resolution videos. To enable such capability, we devise a novel Compressive Packed Inference to minimize per-pixel processing costs by selectively determining the necessary pixels to process and combining them to maximize processing parallelism. In particular, our system quickly extracts ROIs and dynamically shrinks them, reflecting the effect of the fast-changing characteristics of objects and scenes. It then intelligently combines such scaled ROIs into large canvases to maximize the utilization of inference accelerators such as GPU. Evaluation across various datasets, models, and devices shows Mondrian outperforms state-of-the-art baselines (e.g., input rescaling, ROI extractions, ROI extractions+batching) by 15.0-19.7% higher accuracy, leading to $\times$6.65 higher throughput than frame-wise inference for processing various 1080p video streams. We will release the code after the paper review.

Taeahn Kwon, Minkyung Jeong, Eon-Suk Ko et al.

To acquire language, children need rich language input. However, many parents find it difficult to provide children with sufficient language input, which risks delaying their language development. To aid these parents, we design Captivate!, the first system that provides contextual language guidance to parents during play. Our system tracks both visual and spoken language cues to infer targets of joint attention, enabling the real-time suggestion of situation-relevant phrases for the parent. We design our system through a user-centered process with immigrant families--a highly vulnerable yet understudied population--as well as professional speech language therapists. Next, we evaluate Captivate! on parents with children aged 1-3 to observe improvements in responsive language use. We share insights into developing contextual guidance technology for linguistically diverse families.