Author name cluster

Qiang Qu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

12 papers

1 author row

EAAI Journal 2026 Journal Article

Physics-guided adaptive confidence network for real-time underwater image restoration

Pan Gao
Qiang Qu
Dan Xiang
Jing Ling
Naiyao Liang

Underwater image restoration remains a persistent challenge due to the spatially heterogeneous nature of light attenuation and scattering. While physics-based methods offer interpretability, they rely on rigid assumptions that often fail in complex turbid regions. Conversely, deep learning approaches offer flexibility but lack the structural constraints necessary for consistent generalization. To resolve this conflict, we propose the Physics-Guided Attention Confidence Network (PGAC-Net), a lightweight framework that unifies physical modeling with data-driven refinement through a novel reliability-aware fusion mechanism. An efficient shared encoder extracts multiple scales features to estimate transmission maps, spatially varying background light, and a pixel-wise confidence map. This confidence map dynamically arbitrates between a physics-based inversion branch and a residual refinement branch, which is specifically designed to correct color casts and restore fine texture details lost in the physical model. Extensive experiments on benchmark datasets demonstrate state-of-the-art performance, achieving a 0. 953 structural similarity index (SSIM) and 26. 686 peak signal-to-noise ratio (PSNR) on the Underwater Image Enhancement Benchmark (UIEB) while maintaining high efficiency with 0. 34 million (M) parameters and 10 ms inference time. The method also exhibits improved color fidelity and structural consistency. The proposed framework is well suited for real-time deployment on resource-constrained autonomous underwater vehicles. Code: https: //github. com/pan-gao0904/PGAC-Net.

Details DOI

AAAI Conference 2026 Conference Paper

Ultralight Polarity-Split Neuromorphic SNN for Event-Stream Super-Resolution

Chuanzhi Xu
Haoxian Zhou
Langyi Chen
Yuk Ying Chung
Qiang Qu

Event cameras offer unparalleled advantages such as high temporal resolution, low latency, and high dynamic range. However, their limited spatial resolution poses challenges for fine-grained perception tasks. In this work, we propose an ultra-lightweight, stream-based event-to-event super-resolution method based on Spiking Neural Networks (SNNs), designed for real-time deployment on resource-constrained devices. To further reduce model size, we introduce a novel Dual-Forward Polarity-Split Event Encoding strategy that decouples positive and negative events into separate forward paths through a shared SNN. Furthermore, we propose a Learnable Spatio-temporal Polarity-aware Loss (LearnSTPLoss) that adaptively balances temporal, spatial, and polarity consistency using learnable uncertainty-based weights. Experimental results demonstrate that our method achieves competitive super-resolution performance on multiple datasets while significantly reducing model size and inference time. The lightweight design enables embedding the module into event cameras or using it as an efficient front-end preprocessing for downstream vision tasks.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Fine-Tuning Language Models with Collaborative and Semantic Experts

Jiaxi Yang
Binyuan Hui
Min Yang
Jian Yang
Lei Zhang
Qiang Qu
Junyang Lin

Recent advancements in large language models (LLMs) have broadened their application scope but revealed challenges in balancing capabilities across general knowledge, coding, and mathematics. To address this, we introduce a Collaborative and Semantic Experts (CoE) approach for supervised fine-tuning (SFT), which employs a two-phase training strategy. Initially, expert training fine-tunes the feed-forward network on specialized datasets, developing distinct experts in targeted domains. Subsequently, expert leveraging synthesizes these trained experts into a structured model with semantic guidance to activate specific experts, enhancing performance and interpretability. Evaluations on comprehensive benchmarks across MMLU, HumanEval, GSM8K, MT-Bench, and AlpacaEval confirm CoE's efficacy, demonstrating improved performance and expert collaboration in diverse tasks, significantly outperforming traditional SFT methods.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs

Lei Zhang
Yunshui Li
Jiaming Li
Xiaobo Xia
Jiaxi Yang
Run Luo
Minzheng Wang
Longze Chen

Some of the latest released Code Large Language Models (Code LLMs) have been trained on repository-level code data, enabling them to perceive repository structures and utilize cross-file code information. This capability allows us to directly concatenate the content of repository code files in prompts to achieve repository-level code completion. However, in real development scenarios, directly concatenating all code repository files in a prompt can easily exceed the context window of Code LLMs, leading to a significant decline in completion performance. Additionally, overly long prompts can increase completion latency, negatively impacting the user experience. In this study, we conducted extensive experiments, including completion error analysis, topology dependency analysis, and cross-file content analysis, to investigate the factors affecting repository-level code completion. Based on the conclusions drawn from these preliminary experiments, we proposed a strategy called **Hierarchical Context Pruning (HCP)** to construct high-quality completion prompts. We applied the **HCP** to six Code LLMs and evaluated them on the CrossCodeEval dataset. The experimental results showed that, compared to previous methods, the prompts constructed using our **HCP** strategy achieved higher completion accuracy on five out of six Code LLMs. Additionally, the **HCP** managed to keep the prompt length around 8k tokens (whereas the full repository code is approximately 50k tokens), significantly improving completion throughput. Our code and data will be publicly available.

PDF Details DOI

TMLR Journal 2025 Journal Article

State space models can express $n$-gram languages

Vinoth Nandakumar
Qiang Qu
Peng Mi
Tongliang Liu

Recent advancements in recurrent neural networks (RNNs) have reinvigorated interest in their application to natural language processing tasks, particularly with the development of more efficient and parallelizable variants known as state space models (SSMs), which have shown competitive performance against transformer models while maintaining a lower memory footprint. While RNNs and SSMs (e.g., Mamba) have been empirically more successful than rule-based systems based on $n$-gram models, a rigorous theoretical explanation for this success has not yet been developed, as it is unclear how these models encode the combinatorial rules that govern the next-word prediction task. In this paper, we construct state space language models that can solve the next-word prediction task for languages generated from $n$-gram rules, thereby showing that the former are more expressive. Our proof shows how SSMs can encode $n$-gram rules using new theoretical results on their memorization capacity, and demonstrates how their context window can be controlled by restricting the spectrum of the state transition matrix. We conduct experiments with a small dataset generated from $n$-gram rules to show how our framework can be applied to SSMs and RNNs obtained through gradient-based optimization.

PDF Details

AAAI Conference 2024 Conference Paper

E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

Qiang Qu
Yiran Shen
Xiaoming Chen
Yuk Ying Chung
Tongliang Liu

The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural spatial-temporal event-streams make it challenging for providing intuitive visualization with rich semantic information for human vision. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. However, current solutions are predominantly data-driven without considering the prior knowledge of the underlying statistics relating event-streams and video frames. It highly relies on the non-linearity and generalization capability of the deep neural networks, thus, is struggling on reconstructing detailed textures when the scenes are complex. In this work, we propose E2HQV, a novel E2V paradigm designed to produce high-quality video frames from events. This approach leverages a model-aided deep learning framework, underpinned by a theory-inspired E2V model, which is meticulously derived from the fundamental imaging principles of event cameras. To deal with the issue of state-reset in the recurrent components of E2HQV, we also design a temporal shift embedding module to further improve the quality of the video frames. Comprehensive evaluations on the real world event camera datasets validate our approach, with E2HQV, notably outperforming state-of-the-art approaches, e.g., surpassing the second best by over 40% for some evaluation metrics.

PDF Details DOI

IJCAI Conference 2020 Conference Paper

AILA: A Question Answering System in the Legal Domain

Weiyi Huang
Jiahao Jiang
Qiang Qu
Min Yang

Question answering (QA) in the legal domain has gained increasing popularity for people to seek legal advice. However, existing QA systems struggle to comprehend the legal context and provide jurisdictionally relevant answers due to the lack of domain expertise. In this paper, we develop an Artificial Intelligence Law Assistant (AILA) for question answering in the domain of Chinese laws. AILA system automatically comprehends users' natural language queries with the help of the legal knowledge graph (KG) and provides the best matching answers for given queries. In addition, AILA provides visual cues to interpret the input queries and candidate answers based on the legal KG. Experimental results on a large-scale legal QA corpus show the effectiveness of AILA. To the best of our knowledge, AILA is the first Chinese legal QA system which integrates the domain knowledge from legal KG to comprehend the questions and answers for ranking QA pairs. AILA is available at http: //bmilab. ticp. io: 48478/.

PDF Details DOI

AAAI Conference 2019 Short Paper

A Multi-Task Learning Approach for Answer Selection: A Study and a Chinese Law Dataset

Wenyu Du
Baocheng Li
Min Yang
Qiang Qu
Ying Shen

In this paper, we propose a Multi-Task learning approach for Answer Selection (MTAS), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains (tasks). Specifically, MTAS consists of two key components: (i) A category classification model that learns rich category-aware document representation; (ii) An answer selection model that provides the matching scores of question-answer pairs. These two tasks work on a shared document encoding layer, and they cooperate to learn a high-quality answer selection system. In addition, a multi-head attention mechanism is proposed to learn important information from different representation subspaces at different positions. We manually annotate the first Chinese question answering dataset in law domain (denoted as LawQA) to evaluate the effectiveness of our model. The experimental results show that our model MTAS consistently outperforms the compared methods. 1

PDF Details

AAAI Conference 2019 Conference Paper

Exploring Human-Like Reading Strategy for Abstractive Text Summarization

Min Yang
Qiang Qu
Wenting Tu
Ying Shen
Zhou Zhao
Xiaojun Chen

The recent artificial intelligence studies have witnessed great interest in abstractive text summarization. Although remarkable progress has been made by deep neural network based methods, generating plausible and high-quality abstractive summaries remains a challenging task. The human-like reading strategy is rarely explored in abstractive text summarization, which however is able to improve the effectiveness of the summarization by considering the process of reading comprehension and logical thinking. Motivated by the humanlike reading strategy that follows a hierarchical routine, we propose a novel Hybrid learning model for Abstractive Text Summarization (HATS). The model consists of three major components, a knowledge-based attention network, a multitask encoder-decoder network, and a generative adversarial network, which are consistent with the different stages of the human-like reading strategy. To verify the effectiveness of HATS, we conduct extensive experiments on two real-life datasets, CNN/Daily Mail and Gigaword datasets. The experimental results demonstrate that HATS achieves impressive results on both datasets.

PDF Details

AAAI Conference 2019 Short Paper

Learning Document Embeddings with Crossword Prediction

Junyu Luo
Min Yang
Ying Shen
Qiang Qu
Haixia Chai

In this paper, we propose a Document Embedding Network (DEN) to learn document embeddings in an unsupervised manner. Our model uses the encoder-decoder architecture as its backbone, which tries to reconstruct the input document from an encoded document embedding. Unlike the standard decoder for text reconstruction, we randomly block some words in the input document, and use the incomplete context information and the encoded document embedding to predict the blocked words in the document, inspired by the crossword game. Thus, our decoder can keep the balance between the known and unknown information, and consider both global and partial information when decoding the missing words. We evaluate the learned document embeddings on two tasks: document classification and document retrieval. The experimental results show that our model substantially outperforms the compared methods. 1.

PDF Details

AAAI Conference 2018 Short Paper

Generative Adversarial Network for Abstractive Text Summarization

Linqing Liu
Yao Lu
Min Yang
Qiang Qu
Jia Zhu
Hongyan Li

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries1.

PDF Details

AAAI Conference 2018 Short Paper

Negative-Aware Influence Maximization on Social Networks

Yipeng Chen
Hongyan Li
Qiang Qu

How to minimize the impact of negative users within the maximal set of inﬂuenced users? The Inﬂuenced Maximization (IM) is important for various applications. However, few studies consider the negative impact of some of the inﬂuenced users. We propose a negative-aware inﬂuence maximization problem by considering users’ negative impact. A novel algorithm is proposed to solve the problem. Experiments on real-world datasets show the proposed algorithm can achieve 70% improvement on average in expected inﬂuence compared with rivals.

PDF Details