Jiaqi Shi

EAAI Journal 2026 Journal Article

An interpretable Transformer–LSTM denoising autoencoder for semi-supervised fault diagnosis in chemical processes

• Lijie Guo
• Jiaqi Shi
• Jianxin Kang
• Ao Li

AAAI Conference 2025 Conference Paper

QuARF: Quality-Adaptive Receptive Fields for Degraded Image Perception

• Fei Gao
• Ying Zhou
• Ziyun Li
• Wenwang Han
• Jiaqi Shi
• Maoying Qiao
• Jinlan Xu
• Nannan Wang

Advanced Deep Neural Networks (DNNs) perform well for high-quality images, but their performance dramatically decreases for degraded images. Data augmentation is commonly used to alleviate this problem, but using too much perturbed data might seriously decrease the performance on pristine images. To tackle this challenge, we take our cue from the assumption of spatial coincidence in human visual perception, i.e. multiscale and varying receptive fields are required for understanding pristine and degraded images. Correspondingly, we propose a novel plug-and-play network architecture, dubbed Quality-Adaptive Receptive Fields (QuARF), to automatically select the optimal receptive fields based on the quality of the input image. To this end, we first design a multi-kernel convolutional block, which comprises multiscale continuous receptive fields. Afterward, we design a quality-adaptive routing network to predict the significance of each kernel, based on the quality features extracted from the input image. In this way, QuARF automatically selects the optimal inference route for each image. To further boost efficiency and effectiveness, the input feature map is split into multiple groups, with each group independently learning its quality-adaptive routing parameters. We apply QuARF to a variety of DNNs and conduct experiments in both discriminative and generation tasks, including semantic segmentation, image translation, and restoration. Thorough experimental results show that QuARF significantly and robustly improves the performance for degraded images, and outperforms data augmentation in most cases.

IROS Conference 2023 Conference Paper

Recognizing Real-World Intentions using A Multimodal Deep Learning Approach with Spatial-Temporal Graph Convolutional Networks

• Jiaqi Shi
• Chaoran Liu
• Carlos Toshinori Ishi
• Bowen Wu 0002
• Hiroshi Ishiguro

Identifying intentions is a critical task for comprehending the actions of others, anticipating their future behavior, and making informed decisions. However, it is challenging to recognize intentions due to the uncertainty of future human activities and the complex influence factors. In this work, we explore the method of recognizing intentions alluded under human behaviors in the real world, aiming to boost intelligent systems' ability to recognize potential intentions and understand human behaviors. We collect data containing real-world human behaviors before using a hand dispenser and a temperature scanner at the building entrance. These data are processed and labeled into intention categories. A questionnaire is conducted to survey the human ability in inferring the intentions of others. Skeleton data and image features are extracted inspired by the answer to the questionnaire. For skeleton-based intention recognition, we propose a spatial-temporal graph convolutional network that performs graph convolutions on both part-based graphs and adaptive graphs, which achieves the best performance compared with baseline models in the same task. A deep-learning-based method using multimodal features is proposed to automatically infer intentions, which is demonstrated to accurately predict intentions based on past behaviors in the experiment, significantly outperforming humans.

IROS Conference 2022 Conference Paper

Controlling the Impression of Robots via GAN-based Gesture Generation

• Bowen Wu 0002
• Jiaqi Shi
• Chaoran Liu
• Carlos Toshinori Ishi
• Hiroshi Ishiguro

As a type of body language, gestures can largely affect the impressions of human-like robots perceived by users. Recent data-driven approaches to the generation of co-speech gestures have successfully promoted the naturalness of produced gestures. These approaches also possess greater generalizability to work under various contexts than rule-based methods. However, most have no direct control over the human impressions of robots. The main obstacle is that creating a dataset that covers various impression labels is not trivial. In this study, based on previous findings in cognitive science on robot impressions, we present a heuristic method to control them without manual labeling, and demonstrate its effectiveness on a virtual agent and partially on a humanoid robot through subjective experiments with 50 participants.