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Xueyi Zhou

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

EdgeMTSC: A Lightweight Large-Kernel ConvNet for Multivariate Time Series Classification

Xueyi Zhou
Zhenyu Li
Dong-Kyu Chae

In large-scale sensor networks, Multivariate Time Series Classification (MTSC) is a pivotal task for identifying events dependent on longitudinal data at the edge. However, existing methods focus on neither the inherent ability of convolutional networks to perceive subsequence features, nor the prolonged processing pipeline and the model deployment overhead brought by the highly parameterized models. To resolve these difficulties, we present EdgeMTSC, a lightweight large-kernel ConvNet for MTSC, which naturally extracts and learns features of diverse subsequences. Specifically, a novel module named Inter-channel Message Passing-driven Kernel Block (IMP-KB) is proposed, which maintains a learnable correlation matrix to propagate and merge inter-channel messages, and fuses miscellaneous patterns learned by parallel conv kernels of different sizes. EdgeMTSC sequences two modules of different receptive fields to aggregate local features using small kernels and study long-term representation provided by large kernels, respectively. For inference parameter reduction and accelerating inference without performance loss, the conv blocks in IMP-KBs are structurally reparameterizable. The performance of our model (76.2%) is benchmarked on 26 UEA MTSC datasets and is superior to the SOTA model (MPTSNet, 75%). At the same time, EdgeMTSC uses the fewest parameters and achieves the minimum inference time, applicable on any machine (8 devices ranging from large-scale distributed AI computing servers to resource-constrained edge devices) and in any application scenario.

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IJCAI Conference 2025 Conference Paper

What If LLMs Can Smell: A Prototype

Xueyi Zhou
Qi Lu
Dong-Kyu Chae

The olfaction is hardly mentioned in the studies of multi-modal Large Language Models (LLMs). This demo presents a prototypical framework to embody prevalent LLMs with smelling ability using a plug-and-play olfactory signal processing service. To this end, we collect a dataset on Korean beers by self-developed electronic noses (e-noses) and an open-source dataset. An olfaction-related question-answering corpus is also generated to fine-tune LLMs. A gas classification model is applied to identify the smelling liquor upon the e-nose data. We then adopt and fine-tune LLMs on the generated datasets. The results show that LLMs under this framework can interact with the environment by its `nose' and provide olfaction-related answers augmented by our dataset. To the best of our knowledge, this is the first work on embodying LLMs with artificial olfaction. We additionally deployed the gas classification model and the trained LLM in a simple web-based system to show the feasibility of our prototype. Our demo video can be found at: https: //bit. ly/4j8x6ZY.

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ICLR Conference 2024 Conference Paper

Towards Category Unification of 3D Single Object Tracking on Point Clouds

Jiahao Nie 0001
Zhiwei He 0001
Xudong Lv
Xueyi Zhou
Dong-Kyu Chae
Fei Xie

Category-specific models are provenly valuable methods in 3D single object tracking (SOT) regardless of Siamese or motion-centric paradigms. However, such over-specialized model designs incur redundant parameters, thus limiting the broader applicability of 3D SOT task. This paper first introduces unified models that can simultaneously track objects across all categories using a single network with shared model parameters. Specifically, we propose to explicitly encode distinct attributes associated to different object categories, enabling the model to adapt to cross-category data. We find that the attribute variances of point cloud objects primarily occur from the varying size and shape (e.g., large and square vehicles v.s. small and slender humans). Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner. We further incorporate the size and shape prior derived from the known template targets into the model’s inputs and learning objective, facilitating the learning of unified representation. Equipped with such designs, we construct two category-unified models SiamCUT and MoCUT. Extensive experiments demonstrate that SiamCUT and MoCUT exhibit strong generalization and training stability. Furthermore, our category-unified models outperform the category-specific counterparts by a significant margin (e.g., on KITTI dataset, $\sim$12\% and $\sim$3\% performance gains on the Siamese and motion paradigms).

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IJCAI Conference 2023 Conference Paper

IMPsys: An Intelligent Mold Processing System for Smart Factory

Xueyi Zhou
Yohan Na
Minju Bang
Dong-Kyu Chae

The explosive popularity of smart manufacturing has caught the attention of researchers in terms of intelligent mold processing and management. Machining mold components is a crucial step in the mold production process for many industries, which creates (e. g. , cutting, drilling, and shaping a metal) the individual parts (e. g. , core pins, ejector pins, cavities, slides, and lifters) that make up a mold used in manufacturing. We present IMPsys, an AI-based system that automatically explores machining jobs, infers their processing time and schedules them on machines, given numerous 3D modelling files of mold components. Our demo video can be found at: http: //bit. ly/3EeKnyL.

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