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Shangguang Wang

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

SCo-Cloud: Satellite Constellation Collaboration for Cloud-Aware Onboard-Computed Imaging and Transmission

Jia Liu
Qian Li
Yongqi Li
Cheng Ji
Shangguang Wang

Satellite-acquired optical remote sensing imagery is extensively applied in time-critical applications like traffic surveillance and evaluation of natural disasters. However, clouds, as a common atmospheric phenomenon, frequently obscure observation. Current approaches aim to restore visibility in cloud-obscured regions, yet they typically fall short in the presence of dense cloud cover, which are exceedingly prevalent in remote sensing imagery. Alternative approaches rely on the satellite revisit cycle, frequently surpassing ten days, a duration impractical for genuine application scenarios due to target changes and bandwidth limitations. To address these issues, this paper proposes SCo-Cloud, a novel satellite constellation collaboration framework for cloud-aware onboard-computed imaging and transmission, which consists of Center-Sat and Edge-Sats. We propose onboard thin cloud removal and re-imaging region location models to locate the impact of clouds. We further design a novel multi-satellite scheduling strategy to eliminate clouds. The models above are integrated within the Center-Sat, with the nearby Edge-Sats collaborating in tandem to execute re-imaging assignments. Furthermore, to facilitate in-depth research, we have meticulously developed a cloud-covered target detection dataset. Comprehensive experiments have conclusively demonstrated that SCo-Cloud effectively surpasses the limitations inherent in current approaches, providing accurate and timely responses within the domain of Earth observation.

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NeurIPS Conference 2025 Conference Paper

Black-Box Membership Inference Attack for LVLMs via Prior Knowledge-Calibrated Memory Probing

Jinhua Yin
Peiru Yang
Chen Yang
Huili Wang
Zhiyang Hu
Shangguang Wang
Yongfeng Huang
Tao Qi

Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data. Empowered by large-scale parameters, these models often exhibit strong memorization of their training data, rendering them susceptible to membership inference attacks (MIAs). Existing MIA methods for LVLMs typically operate under white- or gray-box assumptions, by extracting likelihood-based features for the suspected data samples based on the target LVLMs. However, mainstream LVLMs generally only expose generated outputs while concealing internal computational features during inference, limiting the applicability of these methods. In this work, we propose the first black-box MIA framework for LVLMs, based on a prior knowledge-calibrated memory probing mechanism. The core idea is to assess the model memorization of the private semantic information embedded within the suspected image data, which is unlikely to be inferred from general world knowledge alone. We conducted extensive experiments across four LVLMs and three datasets. Empirical results demonstrate that our method effectively identifies training data of LVLMs in a purely black-box setting and even achieves performance comparable to gray-box and white-box methods. Further analysis reveals the robustness of our method against potential adversarial manipulations, and the effectiveness of the methodology designs. Our code and data are available at \url{https: //github. com/spmede/KCMP}.

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NeurIPS Conference 2025 Conference Paper

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades

Yanan Li
Fanxu Meng
Muhan Zhang
Shiai Zhu
Shangguang Wang
Mengwei Xu

As Large Language Models (LLMs) are frequently updated, LoRA weights trained on earlier versions quickly become obsolete. The conventional practice of retraining LoRA weights from scratch on the latest model is costly, time-consuming, and environmentally detrimental, particularly as the diversity of LLMs and downstream tasks expands. This motivates a critical question: "How can we efficiently leverage existing LoRA weights to adapt to newer model versions? " To address this, we propose LoRASuite, a modular approach tailored specifically to various types of LLM updates. First, we compute a transfer matrix utilizing known parameters from both old and new LLMs. Next, we allocate corresponding layers and attention heads based on centered kernel alignment and cosine similarity metrics, respectively. A subsequent small-scale, skillful fine-tuning step ensures numerical stability. Experimental evaluations demonstrate that LoRASuite consistently surpasses small-scale vanilla LoRA methods. Notably, on backbone LLMs such as MiniCPM and Qwen, LoRASuite even exceeds the performance of full-scale LoRA retraining, with average improvements of +1. 4 and +6. 6 points on math tasks, respectively. Additionally, LoRASuite significantly reduces memory consumption by 5. 5 GB and computational time by 78. 23%.

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IJCAI Conference 2025 Conference Paper

Multimodal Knowledge Retrieval-Augmented Iterative Alignment for Satellite Commonsense Conversation

Qian Li
Xuchen Li
Zongyu Chang
Yuzheng Zhang
Cheng Ji
Shangguang Wang

Satellite technology has significantly influenced our daily lives, manifested in applications such as navigation and communication. With its development, a vast amount of multimodal satellite commonsense data has been generated, thus leading to an urgent demand for conversation about satellite data. However, existing large language models suffer from prevalent hallucinations and poor comprehensibility on multimodal satellite data due to their high professional content threshold and partial information opacity. To address these issues, we propose a multimodal satellite knowledge retrieval-augmented iterative alignment framework (Sat-RIA) for satellite commonsense conversation. We first construct multi-view retrieval expert knowledge to reduce hallucinations and enhance the interpretability of responses, which incorporates the satellite expert database, satellite rule, satellite image database, and a satellite knowledge graph. We next design commonsense conversation instructions to make the answers more legible and understandable. Furthermore, the retrieval-augmented iterative alignment module refines response precision by aligning outputs with task-specific standards through multi-stage evaluations. Finally, we construct satellite multi-turn dialogue and visual question-answer datasets for a more comprehensive evaluation of satellite commonsense conversation. Experimental results demonstrate that Sat-RIA outperforms existing large language models and provides more comprehensible answers with fewer hallucinations.

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IJCAI Conference 2025 Conference Paper

Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction

Qian Li
Cheng Ji
Shu Guo
Kun Peng
Qianren Mao
Shangguang Wang

Multi-modal relation extraction (MMRE) is a challenging task that seeks to identify relationships between entities with textual and visual attributes. However, existing methods struggle to handle the complexities posed by multiple entity pairs within a single sentence that share similar contextual information (e. g. , identical text and image content). These scenarios amplify the difficulty of distinguishing relationships and hinder accurate extraction. To address these limitations, we propose the variational multi-modal hypergraph attention network (VM-HAN), a novel and robust framework for MMRE. Unlike previous approaches, VM-HAN constructs a multi-modal hypergraph for each sentence-image pair, explicitly modeling high-order intra-/inter-modal correlations among different entity pairs in the same context. This design enables a more detailed and nuanced understanding of entity relationships by capturing intricate cross-modal interactions that are often overlooked. Additionally, we introduce the variational hypergraph attention network (V-HAN). This variational attention mechanism dynamically refines the hypergraph structure, enabling the model to effectively handle the inherent ambiguity and complexity of multi-modal data. Comprehensive experiments on benchmark MMRE datasets demonstrate that VM-HAN achieves state-of-the-art performance, significantly surpassing existing methods in both accuracy and efficiency.

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NeurIPS Conference 2024 Conference Paper

SILENCE: Protecting privacy in offloaded speech understanding on resource-constrained devices

Dongqi Cai
Shangguang Wang
Zeling Zhang
Felix Xiaozhu Lin
Mengwei Xu

Speech serves as a ubiquitous input interface for embedded mobile devices. Cloud-based solutions, while offering powerful speech understanding services, raise significant concerns regarding user privacy. To address this, disentanglement-based encoders have been proposed to remove sensitive information from speech signals without compromising the speech understanding functionality. However, these encoders demand high memory usage and computation complexity, making them impractical for resource-constrained wimpy devices. Our solution is based on a key observation that speech understanding hinges on long-term dependency knowledge of the entire utterance, in contrast to privacy-sensitive elements that are short-term dependent. Exploiting this observation, we propose SILENCE, a lightweight system that selectively obscuring short-term details, without damaging the long-term dependent speech understanding performance. The crucial part of SILENCE is a differential mask generator derived from interpretable learning to automatically configure the masking process. We have implemented SILENCE on the STM32H7 microcontroller and evaluate its efficacy under different attacking scenarios. Our results demonstrate that SILENCE offers speech understanding performance and privacy protection capacity comparable to existing encoders, while achieving up to 53. 3$\times$ speedup and 134. 1$\times$ reduction in memory footprint.

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IJCAI Conference 2015 Conference Paper

Re-Ranking Voting-Based Answers by Discarding User Behavior Biases

Xiaochi Wei
Heyan Huang
Chin-Yew Lin
Xin Xin
Xianling Mao
Shangguang Wang

The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user’s attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15. 1% in precision@1, and 11. 7% in the mean reciprocal rank. A case st-udy on a manually labeled dataset futher supports the effectiveness of the proposed model.

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