arXiv subjects

Yaning Li

Publications and source records attributed to Yaning Li.

10 recordsLinked to original sources

arXiv2026

Domain-Incremental Remote Sensing Change Detection via Difference-Guided Adaptation and Frequency-Decoupled Distillation

Remote sensing change detection (RSCD) models are prone to catastrophic forgetting when incrementally adapted to new domains. Existing domain-incremental learning (DIL) methods mainly preserve image-level representations but often overlook bitemporal discrepancy cues, which are critical for robust change detection under domain shifts. To address this limitation, we propose DG-FDD, a domain-incremental change detection framework that integrates Difference-Guided Adaptation and Frequency-Decoupled Distillation. Specifically, the Difference-Guided Dynamic Adapter (DGDA) models bitemporal feature discrepancies to promote change-aware feature adaptation and reduce domain-specific interference. Meanwhile, the Frequency-Decoupled Knowledge Distillation strategy with Cross-domain Synthesis (FDKD-CS) separates structural information from domain style in the frequency domain, enabling stable knowledge transfer without historical data. Extensive experiments on three public high-resolution RSCD datasets under two- and three-domain incremental protocols demonstrate that DG-FDD effectively mitigates catastrophic forgetting. Compared with independently trained single-task models, DG-FDD records mean relative changes in F1 and IoU of only -0.23% and -0.45%, respectively, across six two-domain sequences, and -0.69% and -1.31%, respectively, across the three evaluated three-domain sequences. These results indicate a favorable stability-plasticity balance between historical knowledge retention and new-domain adaptation in continual cross-domain change detection.

cs.CV↗

arXiv2025

From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning

The evolution of cluster structure with size and the critical size for the transition from cluster to nanocrystal have long been fundamental problems in nanoscience. Due to limitations of experimental technology and computational methods, the exploration of the continuous evolution of clusters towards nanocrystal is still a big challenge. Here, we proposed a machine learning force field (MLFF) that can generalize well to various copper systems ranging from small clusters to large clusters and bulk. The continuous evolution of copper clusters CuN towards nanocrystal was revealed by investigating clusters in a wide size range (7 <= N <= 17885) based on MLFF simulated annealing. For small CuN (N < 40), electron counting rule plays a major role in stability. For large CuN (N > 80), geometric magic number rule plays a dominant role and the evolution of clusters is based on the formation of more and more icosahedral shells. For medium size CuN (40 <= N <= 80), both rules contribute. The critical size from cluster to nanocrystal was calculated to be around 8000 atoms (about 6 nm in diameter). Our work terminates the long-term challenge in nanoscience, and lay the methodological foundation for subsequent research on other cluster systems.

cond-mat.mtrl-sci↗

arXiv2025

SMRABooth: Subject and Motion Representation Alignment for Customized Video Generation

Customized video generation aims to produce videos that faithfully preserve the subject's appearance from reference images while maintaining temporally consistent motion from reference videos. Existing methods struggle to ensure both subject appearance similarity and motion pattern consistency due to the lack of object-level guidance for subject and motion. To address this, we propose SMRABooth, which leverages the self-supervised encoder and optical flow encoder to provide object-level subject and motion representations. These representations are aligned with the model during the LoRA fine-tuning process. Our approach is structured in three core stages: (1) We exploit subject representations via a self-supervised encoder to guide subject alignment, enabling the model to capture overall structure of subject and enhance high-level semantic consistency. (2) We utilize motion representations from an optical flow encoder to capture structurally coherent and object-level motion trajectories independent of appearance. (3) We propose a subject-motion association decoupling strategy that applies sparse LoRAs injection across both locations and timing, effectively reducing interference between subject and motion LoRAs. Extensive experiments show that SMRABooth excels in subject and motion customization, maintaining consistent subject appearance and motion patterns, proving its effectiveness in controllable text-to-video generation.

cs.CV↗

arXiv2025

Pre/Absence: Prompting Cultural Awareness and Understanding for Lost Architectural Heritage in Virtual Reality

Lost architectural heritage presents interpretive challenges due to vanished structures and fragmented historical records. Using Hanyuan Hall of the Tang dynasty's Daming Palace as a case study, we conducted a formative investigation with archaeologists, heritage administrators, and visitors to identify key issues in current interpretation practices. We found that these practices often compress complex cultural layers into factual summaries and rely on linear narratives that overlook the continuing reinterpretations following a site's disappearance. In response, we designed Pre/Absence, a virtual reality experience grounded in the presence-absence dialectic to interweave tangible and vanished aspects of heritage within a spatiotemporal narrative. A mixed-method study with 28 participants compared Pre/Absence to a paper-based experience. Both improved users' factual understanding, but the VR experience more strongly enhanced cultural awareness, evoked emotional engagement with loss, and encouraged critical reflection on the evolving social and political meanings of heritage. The findings suggest that VR can move beyond static reconstruction to engage users as co-constructors of cultural meaning, providing a nuanced framework for critical heritage narrative design in human-computer interaction.

cs.HC↗

arXiv2025

LacAIDes: Generative AI-Supported Creative Interactive Circuits Crafting to Enliven Traditional Lacquerware

Lacquerware, a representative craft of Chinese intangible cultural heritage, is renowned for its layered aesthetics and durability but faces declining engagement. While prior human-computer interaction research has explored embedding interactive circuits to transform lacquerware into responsive artifacts, most studies have focused on fabrication techniques rather than supporting makers in creatively designing such interactions at a low threshold. To address this gap, we present LacAIDes, a Generative AI powered creativity-support tool built on a multi-agent workflow aligned with the double diamond model of design thinking. LacAIDes enables exploration and creation of culturally grounded interactive circuits without requiring prior technical expertise. We evaluated LacAIDes in a longitudinal workshop with 34 participants using a mixed-method approach. Results show that LacAIDes demonstrated high usability, enhanced creative engagement in craft making, and encouraged critical reflection on the role of Generative AI in digital craft practices. This work contributes to human-computer interaction by introducing a novel creativity-support tool and providing empirical insights into revitalizing traditional craft making through Generative AI.

cs.HC↗

arXiv2025

"If I were in Space": Understanding and Adapting to Social Isolation through Designing Collaborative Narratives

Social isolation can lead to pervasive health issues like anxiety and loneliness. Previous work focused on physical interventions like exercise and teleconferencing, but overlooked the narrative potential of adaptive strategies. To address this, we designed a collaborative online storytelling experience in social VR, enabling participants in isolation to design an imaginary space journey as a metaphor for quarantine, in order to learn about their isolation adaptation strategies in the process. Eighteen individuals participated during real quarantine undertaken a virtual role-play experience, designing their own spaceship rooms and engaging in collaborative activities that revealed creative adaptative strategies. Qualitative analyses of participant designs, transcripts, and interactions revealed how they coped with isolation, and how the engagement unexpectedly influenced their adaptation process. This study shows how designing playful narrative experiences, rather than solution-driven approaches, can serve as probes to surface how people navigate social isolation.

cs.HC↗

arXiv2022

Epipolar Focus Spectrum: A Novel Light Field Representation and Application in Dense-view Reconstruction

Existing light field representations, such as epipolar plane image (EPI) and sub-aperture images, do not consider the structural characteristics across the views, so they usually require additional disparity and spatial structure cues for follow-up tasks. Besides, they have difficulties dealing with occlusions or larger disparity scenes. To this end, this paper proposes a novel Epipolar Focus Spectrum (EFS) representation by rearranging the EPI spectrum. Different from the classical EPI representation where an EPI line corresponds to a specific depth, there is a one-to-one mapping from the EFS line to the view. Accordingly, compared to a sparsely-sampled light field, a densely-sampled one with the same field of view (FoV) leads to a more compact distribution of such linear structures in the double-cone-shaped region with the identical opening angle in its corresponding EFS. Hence the EFS representation is invariant to the scene depth. To demonstrate its effectiveness, we develop a trainable EFS-based pipeline for light field reconstruction, where a dense light field can be reconstructed by compensating the "missing EFS lines" given a sparse light field, yielding promising results with cross-view consistency, especially in the presence of severe occlusion and large disparity. Experimental results on both synthetic and real-world datasets demonstrate the validity and superiority of the proposed method over SOTA methods.

eess.IV↗

arXiv2021

Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum

The paper aims at removing the aliasing effects of the whole focal stack generated from a sparse-sampled {4D} light field, while keeping the consistency across all the focal layers. We first explore the structural characteristics embedded in the focal stack slice and its corresponding frequency-domain representation, i.e., the Focal Stack Spectrum (FSS). We observe that the energy distribution of the FSS always resides within the same triangular area under different angular sampling rates, additionally the continuity of the Point Spread Function (PSF) is intrinsically maintained in the FSS. Based on these two observations, we propose a learning-based FSS reconstruction approach for one-time aliasing removing over the whole focal stack. Moreover, a novel conjugate-symmetric loss function is proposed for the optimization. Compared to previous works, our method avoids an explicit depth estimation, and can handle challenging large-disparity scenarios. Experimental results on both synthetic and real light field datasets show the superiority of the proposed approach for different scenes and various angular sampling rates.

eess.IV↗

arXiv2020

AIM 2020: Scene Relighting and Illumination Estimation Challenge

We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage.

cs.CV↗

arXiv2019

Structured Illumination Microscopy using Digital Micro-mirror Device and Coherent Light Source

Structured illumination microscopy (SIM) achieves doubled spatial resolution by exciting the specimen with a high-contrast, high-frequency sinusoidal pattern. Such an excitation pattern can be generated by interference between multiple laser beams, which are diffracted from a grating. In SIM, 2D imaging requires 9 patterns and 3D imaging requires 15 patterns. Compared to mechanical movement of gratings, opti-electro devices provide rapid switch of the excitation patterns, in which Digital Micro-mirror Device (DMD) is most common in industry. Here we model DMD as the blazed grating and report a fast and cost-efficient SIM. Our home-built DMD-based laser interference structured illumination microscopy (DMD-ISIM) system reveals the nuclear pore complex and microtubule in mammalian cells with doubled spatial resolution. We further proposed multi-color DMD-ISIM system with simulation, which could potentially exploit the full power of DMD-ISIM.

physics.optics↗