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Chenwei Zhang

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4 papers

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EAAI Journal 2026 Journal Article

Cross-modal feature fusion and distillation for enhanced quantification accuracy in laser-induced breakdown spectroscopy and near-infrared spectroscopy

Weiran Song
Zongyu Hou
Weilun Gu
Minbo Ma
Chenwei Zhang
Jianchao Song
Fei Rao
Hui Wang

Laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIRS) are complementary spectroscopic techniques that provide elemental and molecular fingerprint information of materials, respectively. The integration of LIBS and NIRS enhances analytical accuracy and broadens application potential, attracting growing interest in scientific and industrial communities. However, existing data fusion methods for LIBS and NIRS often process each modality separately, neglecting cross-modal correlations and leaving the intrinsic connections between atomic emission lines and molecular absorption bands unexplored. To address this limitation, we propose a bidirectional cross-attention feature fusion network (Bi-CAFF), a novel data fusion method for improving quantification accuracy in LIBS-NIRS fused data analysis. Bi-CAFF utilises a bidirectional cross-attention mechanism to establish interactive feature correlations between LIBS and NIRS modalities. Importantly, we interpret these interactions based on physical knowledge, revealing a meaningful relationship between LIBS atomic emission lines and NIRS molecular absorption bands. Additionally, we introduce spectral feature distillation (SFD), where a student network trained on NIRS data learns refined features from a LIBS-based teacher model. This approach enhances the quantification accuracy of portable NIRS systems while preserving their cost-efficiency and field-deployment capabilities. Evaluations on industrial datasets show that Bi-CAFF outperforms baseline fusion methods, reducing mean absolute error (MAE) by 12. 1–60. 3 % and root-mean-square error (RMSE) by 12. 4–54. 1 %. Moreover, SFD achieves MAE and RMSE reductions of 6. 6–49. 2 % and13. 1–46. 7 %, respectively, compared to standalone NIRS-based models. Together, these innovations advance the state-of-the-art in machine learning-assisted chemical analysis, laying the groundwork for a new paradigm in multimodal spectral data analysis.

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AAAI Conference 2025 Conference Paper

SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection

Ruoyu Xu
Zhiyu Xiang
Chenwei Zhang
Hanzhi Zhong
Xijun Zhao
Ruina Dang
Peng Xu
Tianyu Pu

3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However, due to the high sparsity and noise associated with the radar point clouds, the performance of the existing methods is still much lower than expected. In this paper, we propose a novel Semi-supervised Cross-modality Knowledge Distillation (SCKD) method for 4D radar-based 3D object detection. It characterizes the capability of learning the feature from a Lidar-radar-fused teacher network with semi-supervised distillation. We first propose an adaptive fusion module in the teacher network to boost its performance. Then, two feature distillation modules are designed to facilitate the cross-modality knowledge transfer. Finally, a semi-supervised output distillation is proposed to increase the effectiveness and flexibility of the distillation framework. With the same network structure, our radar-only student trained by SCKD boosts the mAP by 10.38% over the baseline and outperforms the state-of-the-art works on the VoD dataset. The experiment on ZJUODset also shows 5.12% mAP improvements on the moderate difficulty level over the baseline when extra unlabeled data are available.

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NeurIPS Conference 2024 Conference Paper

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

Yilun Jin
Zheng Li
Chenwei Zhang
Tianyu Cao
Yifan Gao
Pratik Jayarao
Mao Li
Xin Liu

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations. Despite the potential, LLMs face unique challenges in online shopping, such as domain-specific concepts, implicit knowledge, and heterogeneous user behaviors. Motivated by the potential and challenges, we propose Shopping MMLU, a diverse multi-task online shopping benchmark derived from real-world Amazon data. Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants. With Shoppping MMLU, we benchmark over 20 existing LLMs and uncover valuable insights about practices and prospects of building versatile LLM-based shop assistants. Shopping MMLU can be publicly accessed at https: //github. com/KL4805/ShoppingMMLU. In addition, with Shopping MMLU, we are hosting a competition in KDD Cup 2024 with over 500 participating teams. The winning solutions and the associated workshop can be accessed at our website https: //amazon-kddcup24. github. io/.

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IJCAI Conference 2020 Conference Paper

Entity Synonym Discovery via Multipiece Bilateral Context Matching

Chenwei Zhang
Yaliang Li
Nan Du
Wei Fan
Philip S. Yu

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SynonymNet to determine whether or not two given entities are synonym with each other. Instead of using entities features, SynonymNet makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4. 16% improvement in terms of Area Under the Curve and 3. 19% in terms of Mean Average Precision compared to the best baseline method.

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