arXiv subjects

Peiyi Zhou

Publications and source records attributed to Peiyi Zhou.

3 recordsLinked to original sources

arXiv2026

IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion

Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriented fusion control. However, existing text-guided fusion methods often rely on shallow semantic-visual interaction and limited attention mechanisms, which restrict their ability to robustly handle complex degradations and fully exploit textual guidance. In this paper, we propose an iterative text-guided image fusion framework that incorporates text-conditioned feature interaction across multiple fusion and refinement stages. The proposed method integrates deepest-level Cross-Attention, multi-scale Cross-Gate Fusion, and stage-specific text-conditioned modulation, allowing the global text embedding to condition hierarchical feature fusion and residual refinement. By repeatedly injecting the pooled text embedding across hierarchical decoder and refinement stages, the proposed framework provides degradation-aware global semantic conditioning while preserving complementary information from the visible and infrared modalities. Experiments on several benchmark datasets show that the proposed method improves several information-preservation and perceptual-quality metrics, while exhibiting metric-dependent trade-offs on some datasets.

cs.CV↗

arXiv2026

A note on diffusive/random-walk behaviour in Metropolis--Hastings algorithms

We prove a general result that if a Metropolis--Hastings algorithm has a proposal that is not geometrically ergodic and the acceptance rate approaches unity at a suitable rate as the state variable becomes large, then the Metropolised chain will also not be geometrically ergodic. Our conditions seem stronger than might be expected, but are shown to be necessary through a counterexample. We then turn our attention to the random walk and guided walk Metropolis algorithms. We show that if the target distribution has polynomial tails the latter converges at twice the polynomial rate of the former, but that if instead the target distribution has strictly convex potential then the random walk Metropolis behaves as a $1/2$-lazy version of the guided walk Metropolis when the state variable is large, and therefore moves at a similar (ballistic) speed.

stat.CO↗

arXiv2025

A new implementation of Network GARCH Model

Volatility clustering and spillovers are key features of real-world financial time series when there are a lot of cross-sectional financial assets. While network analysis helps connect stocks that are 'similar' or 'correlated', which is effective to link volatility spillovers between stocks, contemporary multivariate ARCH-GARCH formulations struggle to represent structured network dependence and remain parsimonious. We introduce the Generalised Network GARCH (GNGARCH) model as a network volatility model that embeds the GARCH dynamics within the Generalised Network Autoregressive (GNAR) framework, to capture the dynamic volatility of financial asset return by both the asset itself and its 'neighbouring' assets from the constructed virtual network. The proposed volatility model GNGARCH also addresses the limitations for current studies of network GARCH by adapting neighbouring volatility persistence, dynamic conditional covariance updates, and allowing higher-order neighbouring effects rather than only immediate neighbours. This paper provides the model derivation, vectorisation and conversion, stationarity conditions, and also an extension by incorporating threshold coefficients to capture leverage effects. We show that the GNGARCH is a valid volatility model satisfying the stylised facts of financial return series through simulation. Parameter estimation is then performed by using squared returns as variance proxy and minimising a loss function that is either mean squared error (MSE) or quasi-likelihood (QLIKE). We apply our model on 75 of the most active US stocks under a virtual network, and highlight the model's ability in volatility estimation and forecast.

stat.ME↗