arXiv subjects

Qiqi Bao

Publications and source records attributed to Qiqi Bao.

6 recordsLinked to original sources

arXiv2026

GRALS: GCN-Guided Redundancy-Aware Local Search for Minimum Vertex Cover

The minimum vertex cover (MVC) problem seeks to identify the smallest set of vertices that cover all edges in an undirected graph. As a fundamental NP-hard combinatorial optimization problem, MVC has been widely studied due to its applications in network analysis and system design. For large-scale instances, local search heuristics are among the most effective approaches, offering a practical trade-off between solution quality and computational efficiency. Most existing high-performance local search algorithms adopt a break-and-repair framework, where the repair process mainly restores feasibility but provides limited opportunities for enhancing the current solution's structure. We introduce GRALS, a local search framework that integrates vertex probability priors learned from a graph convolutional network with an expansion revelation elimination (ERE) operator. The learned priors guide the search toward promising regions, while the ERE operator dynamically identifies and eliminates redundant vertices to further enhance solution quality. Experiments conducted on 346 benchmark instances demonstrate that GRALS achieves the best-known solution for 335 instances, compared to 302 instances achieved by the strongest baseline. The advantage of GRALS is maintained across different time limits and is particularly evident on large-scale graphs with millions of vertices.

cs.AI↗

arXiv2025

Exact Optimization for Minimum Dominating Sets

The Minimum Dominating Set (MDS) problem is a well-established combinatorial optimization problem with numerous real-world applications. Its NP-hard nature makes it increasingly difficult to obtain exact solutions as the graph size grows. This paper introduces ParDS, an exact algorithm developed to address the MDS problem within the branch-and-bound framework. ParDS features two key innovations: an advanced linear programming technique that yields tighter lower bounds and a set of novel reduction rules that dynamically simplify instances throughout the solving process. Compared to the leading exact algorithms presented at IJCAI 2023 and 2024, ParDS demonstrates theoretically superior lower-bound quality. Experimental results on standard benchmark datasets highlight several significant advantages of ParDS: it achieves fastest solving times in 70% of graph categories, especially on large, sparse graphs, delivers a speed-up of up to 3,411 times on the fastest individual instance, and successfully solves 16 out of 43 instances that other algorithms were unable to resolve within the 5-hour time limit. These findings establish ParDS as a state-of-the-art solution for exactly solving the MDS problem.

cs.DS↗

arXiv2025

Elucidating the solution space of extended reverse-time SDE for diffusion models

Sampling from Diffusion Models can alternatively be seen as solving differential equations, where there is a challenge in balancing speed and image visual quality. ODE-based samplers offer rapid sampling time but reach a performance limit, whereas SDE-based samplers achieve superior quality, albeit with longer iterations. In this work, we formulate the sampling process as an Extended Reverse-Time SDE (ER SDE), unifying prior explorations into ODEs and SDEs. Theoretically, leveraging the semi-linear structure of ER SDE solutions, we offer exact solutions and approximate solutions for VP SDE and VE SDE, respectively. Based on the approximate solution space of the ER SDE, referred to as one-step prediction errors, we yield mathematical insights elucidating the rapid sampling capability of ODE solvers and the high-quality sampling ability of SDE solvers. Additionally, we unveil that VP SDE solvers stand on par with their VE SDE counterparts. Based on these findings, leveraging the dual advantages of ODE solvers and SDE solvers, we devise efficient high-quality samplers, namely ER-SDE-Solvers. Experimental results demonstrate that ER-SDE-Solvers achieve state-of-the-art performance across all stochastic samplers while maintaining efficiency of deterministic samplers. Specifically, on the ImageNet $128\times128$ dataset, ER-SDE-Solvers obtain 8.33 FID in only 20 function evaluations. Code is available at \href{https://github.com/QinpengCui/ER-SDE-Solver}{https://github.com/QinpengCui/ER-SDE-Solver}

cs.LG↗

arXiv2025

Pose Magic: Efficient and Temporally Consistent Human Pose Estimation with a Hybrid Mamba-GCN Network

Current state-of-the-art (SOTA) methods in 3D Human Pose Estimation (HPE) are primarily based on Transformers. However, existing Transformer-based 3D HPE backbones often encounter a trade-off between accuracy and computational efficiency. To resolve the above dilemma, in this work, we leverage recent advances in state space models and utilize Mamba for high-quality and efficient long-range modeling. Nonetheless, Mamba still faces challenges in precisely exploiting local dependencies between joints. To address these issues, we propose a new attention-free hybrid spatiotemporal architecture named Hybrid Mamba-GCN (Pose Magic). This architecture introduces local enhancement with GCN by capturing relationships between neighboring joints, thus producing new representations to complement Mamba's outputs. By adaptively fusing representations from Mamba and GCN, Pose Magic demonstrates superior capability in learning the underlying 3D structure. To meet the requirements of real-time inference, we also provide a fully causal version. Extensive experiments show that Pose Magic achieves new SOTA results ($\downarrow 0.9 mm$) while saving $74.1\%$ FLOPs. In addition, Pose Magic exhibits optimal motion consistency and the ability to generalize to unseen sequence lengths.

cs.CV↗

arXiv2024

Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs

Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities. However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and performance. Typically, they either neglect to exploit the potential of existing extensive pretrained models, limiting their generative capacity, or they necessitate a dozens of forward passes starting from random noises, compromising inference efficiency. In this paper, we present DoSSR, a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images. At the core of our approach is a domain shift equation that integrates seamlessly with existing diffusion models. This integration not only improves the use of diffusion prior but also boosts inference efficiency. Moreover, we advance our method by transitioning the discrete shift process to a continuous formulation, termed as DoS-SDEs. This advancement leads to the fast and customized solvers that further enhance sampling efficiency. Empirical results demonstrate that our proposed method achieves state-of-the-art performance on synthetic and real-world datasets, while notably requiring only 5 sampling steps. Compared to previous diffusion prior based methods, our approach achieves a remarkable speedup of 5-7 times, demonstrating its superior efficiency. Code: https://github.com/QinpengCui/DoSSR.

cs.CV↗

arXiv2022

RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark

Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications. The dataset, code, and models are publicly available at https://github.com/dengzhuo-AI/Real-Fundus

eess.IV↗