arXiv subjects

Haoyang Yang

Publications and source records attributed to Haoyang Yang.

At least 19 recordsLinked to original sources

arXiv2026

AeroReformer2: Spoken-Query Referring Segmentation for Aerial Images

Spoken language offers a natural, hands-free interface for specifying an arbitrary target in dense remote-sensing imagery, yet existing referring remote-sensing image segmentation benchmarks accept only written expressions. To bridge this gap, we introduce \dataset, a spoken-query benchmark derived from RISBench that adds accent- and voice-diverse speech while preserving the original image, mask, and data splits. Its hard evaluation sets combine rotor, wind, and mixed interference with three signal-to-noise levels. We also propose \model, an efficient bilateral network that combines a boundary-preserving visual path with token-preserving speech encoding, kernel linear cross-modal attention, and a resolution refinement head. The design conditions visual features at two scales without materializing a dense speech--visual affinity matrix, then restores fine boundaries using high-resolution visual features. On the clean test split, \model with Swin-Base achieves 62.09\% mean intersection over union (mIoU) and 68.22\% overall intersection over union (oIoU), outperforming the strongest audio-adapted remote-sensing baseline by 5.38 and 2.08 percentage points, respectively. It retains the best hard-set mIoU at 54.09\%. To the best of our knowledge, this is the first benchmark and model study of full-sentence spoken-query referring segmentation for remote-sensing imagery. The code will be made publicly available.

cs.CV↗

arXiv2026

CrossAtlas: Evaluating Projection Techniques for Spatial Referencing in Cross-Reality Collaboration

Cross-reality collaboration increasingly connects immersive and desktop users within synchronized workspaces, yet little is known about how bidirectional projection techniques between immersive 3D layouts and desktop 2D views influence communication. Spatial referencing depends on shared spatial understanding, but different mappings preserve and distort geometric relationships in different ways, altering perceived adjacency, orientation, and coverage across collaborators' views. We present CrossAtlas, a synchronized PC-VR collaboration platform that integrates multiple bidirectional projection techniques, including three planar projection variants and equirectangular, a spherical projection variant, across layouts of varying curvature. In a controlled study with 24 dyads, collaborators completed spatial referencing tasks under different projection-layout conditions while we collected performance and subjective measures. Our results show that projection choice strongly shaped collaboration, with the spherical variant often outperforming planar projections and remaining robust across object layouts.

cs.HC↗

arXiv2026

Real-Time Auto-Optimization in Unknown Environments via Structure-Exploiting Dual Control for Exploration and Exploitation

This paper develops a fast numerical dual control for exploration and exploitation (DCEE) method to address auto-optimization problems in unknown environments. In auto-optimization problems, the optimal operating condition is unknown a priori and may vary with the environment. As in classical dual control techniques, computational burden remains a major concern in DCEE for active learning. Existing DCEE methods provide a principled exploration-exploitation objective, but mainly realized through standard optimization packages or explicit gradient-type update laws, where the numerical structure of the DCEE has not been fully exploited. This paper shows that the reward function in DCEE has an inherent convex-over-nonlinear structure, where the exploitation and exploration terms form a unified nonlinear residual map equipped with a convex outer loss. Benefiting from this structure, a structure-exploiting numerical method is developed by linearizing only the nonlinear residual map while preserving the convex outer loss. Thus, each subproblem is transformed into a structured convex form that can be solved reliably. The resulting generalized Gauss-Newton Hessian approximation is positive semidefinite and depends only on first-order derivatives, thereby supporting fast online computation. The proposed method is evaluated on a vehicle cruising auto-optimization problem and compared with existing methods. Simulation and hardware-in-the-loop experimental results show that the proposed method improves control performance and achieves a speedup of approximately one order of magnitude, with a microsecond-level maximum computation time of only 83 {\mu}s on a typical vehicle embedded CPU.

cs.RO↗

arXiv2026

A Survey of Large Language Models for Perception and Measurement of Human Psychology

Against the backdrop of the rapid advancement of Large Language Models (LLMs), their application in the field of psychology has garnered significant academic attention. A central issue is whether LLMs possess the capability to accurately perceive and measure complex, latent human psychological constructs, such as personality, emotions, and cognitive states. This paper provides a systematic review focused on the use of LLMs as instruments for human psychological measurement. To organize this domain, we propose a comprehensive analytical framework structured around three critical dimensions: Theoretical Plausibility (why measurement might be possible), Measurement Methodology (how to measure), and Application Effectiveness (what has been measured). We first explore the theoretical foundations supporting LLM-based measurement, examining the debate on their emergent cognitive properties from a psychometric perspective. Next, we systematically analyze existing measurement paradigms, categorizing them into active conversational assessment, passive natural language analysis, and multimodal fusion. Subsequently, we review the practical effectiveness and limitations of LLMs in core application areas, including personality trait assessment and mental health evaluation. Distinct from prior reviews focusing on general applications or the ``psychology'' of LLMs themselves, this paper centers on the psychometric properties of LLMs as measurement tools.

cs.CY↗

arXiv2026

SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation

Vision-Language Navigation (VLN) approaches have currently followed two primary paradigms: the end-to-end Vision-Language Model (VLM) policy fine-tuned on navigation trajectories to directly predict actions, and the zero-shot modular pipeline integrating pre-trained Multimodal Large Language Model (MLLM) for training-free generalization to unseen environments. However, end-to-end methods struggle with long-horizon navigation and lack dynamic reasoning, whereas zero-shot methods are constrained by limited spatial grounding for reliable planning and also require substantial reasoning time. To bridge this gap, we introduce SEDualVLN, a spatially-enhanced dual-system VLN framework. System 1 is a VLM model enhanced with both global and local spatial awareness, used for action generation. System 2 integrates a general MLLM with a mapping module, wherein the MLLM plans waypoints by leveraging top-down views of the real-time 3D map alongside streams of rendered path images. Both systems leverage different forms of spatial enhancement to cultivate the agent's sense of direction in VLN tasks. Ultimately, they cooperate to complete the navigation task through a fast-slow coordinated approach. SEDualVLN achieves state-of-the-art performance on VLN-CE benchmarks, and further ablation studies demonstrate the effectiveness of each system and module.

cs.RO↗

arXiv2026

AERR-Nav: Adaptive Exploration-Recovery-Reminiscing Strategy for Zero-Shot Object Navigation

Zero-Shot Object Navigation (ZSON) in unknown multi-floor environments presents a significant challenge. Recent methods, mostly based on semantic value greedy waypoint selection, spatial topology-enhanced memory, and Multimodal Large Language Model (MLLM) as a decision-making framework, have led to improvements. However, these architectures struggle to balance exploration and exploitation for ZSON when encountering unseen environments, especially in multi-floor settings, such as robots getting stuck at narrow intersections, endlessly wandering, or failing to find stair entrances. To overcome these challenges, we propose AERR-Nav, a Zero-Shot Object Navigation framework that dynamically adjusts its state based on the robot's environment. Specifically, AERR-Nav has the following two key advantages: (1) An Adaptive Exploration-Recovery-Reminiscing Strategy, enables robots to dynamically transition between three states, facilitating specialized responses to diverse navigation scenarios. (2) An Adaptive Exploration State featuring Fast and Slow-Thinking modes helps robots better balance exploration, exploitation, and higher-level reasoning based on evolving environmental information. Extensive experiments on the HM3D and MP3D benchmarks demonstrate that our AERR-Nav achieves state-of-the-art performance among zero-shot methods. Comprehensive ablation studies further validate the efficacy of our proposed strategy and modules.

cs.RO↗

arXiv2026

Break the Window: Exploring Spatial Decomposition of Webpages in XR

Most XR web browsers still present webpages as a single floating window, carrying over desktop design assumptions into immersive space. We explore an alternative by breaking the browser window and distributing a webpage into spatial UI chunks within a mixed-reality workspace. We present Break-the-Window (BTW), an exploratory prototype that spatially decomposes live, fully functional webpages into movable panels supporting mid-air and surface-attached placement, as well as direct touch and ray-based interaction. Through a formative study with XR practitioners and an exploratory qualitative study with 15 participants, we observed how spatial decomposition supports distributed attention and spatial meaning-making, while also surfacing challenges around coordination effort, interaction precision, and the lack of shared spatial UI conventions. This work invites discussion on how web interfaces might be reimagined for spatial computing beyond the single-window paradigm.

cs.HC↗

arXiv2025

RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media

We introduce RedNote-Vibe, a dataset spanning five years (pre-LLM to July 2025) sourced from lifestyle platform RedNote (Xiaohongshu), capturing the temporal dynamics of content creation and is enriched with comprehensive engagement metrics. To address the detection challenge posed by RedNote-Vibe, we propose the \textbf{PsychoLinguistic AIGT Detection Framework (PLAD)}. Grounded in cognitive psychology, PLAD leverages deep psychological signatures for robust and interpretable detection. Our experiments demonstrate PLAD's superior performance and reveal insights into content dynamics: (1) human content continues to outperform AI in emotionally resonant domains; (2) AI content is more homogeneous and rarely produces breaking posts, however, this human-AI gap narrows for arousing higher-investment interactions; and (3) most interestingly, a small group of users who strategically utilize AI tools can achieve higher engagement outcomes. The dataset is available at https://github.com/ydli-ai/RedNote-Vibe

cs.CL↗

arXiv2025

LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking

Exploring and comprehending relevant academic literature is a vital yet challenging task for researchers, especially given the rapid expansion in research publications. This task fundamentally involves sensemaking - interpreting complex, scattered information sources to build understanding. While emerging immersive analytics tools have shown cognitive benefits like enhanced spatial memory and reduced mental load, they predominantly focus on information synthesis (e.g., organizing known documents). In contrast, the equally important information foraging phase - discovering and gathering relevant literature - remains underexplored within immersive environments, hindering a complete sensemaking workflow. To bridge this gap, we introduce LitForager, an interactive literature exploration tool designed to facilitate information foraging of research literature within an immersive sensemaking workflow using network-based visualizations and multimodal interactions. Developed with WebXR and informed by a formative study with researchers, LitForager supports exploration guidance, spatial organization, and seamless transition through a 3D literature network. An observational user study with 15 researchers demonstrated LitForager's effectiveness in supporting fluid foraging strategies and spatial sensemaking through its multimodal interface.

cs.HC↗

arXiv2025

ComplicitSplat: Downstream Models are Vulnerable to Blackbox Attacks by 3D Gaussian Splat Camouflages

As 3D Gaussian Splatting (3DGS) gains rapid adoption in safety-critical tasks for efficient novel-view synthesis from static images, how might an adversary tamper images to cause harm? We introduce ComplicitSplat, the first attack that exploits standard 3DGS shading methods to create viewpoint-specific camouflage - colors and textures that change with viewing angle - to embed adversarial content in scene objects that are visible only from specific viewpoints and without requiring access to model architecture or weights. Our extensive experiments show that ComplicitSplat generalizes to successfully attack a variety of popular detector - both single-stage, multi-stage, and transformer-based models on both real-world capture of physical objects and synthetic scenes. To our knowledge, this is the first black-box attack on downstream object detectors using 3DGS, exposing a novel safety risk for applications like autonomous navigation and other mission-critical robotic systems.

cs.CV↗

arXiv2025

3D Gaussian Splat Vulnerabilities

With 3D Gaussian Splatting (3DGS) being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first attack that leverages view-dependent Gaussian appearances - colors and textures that change with viewing angle - to embed adversarial content visible only from specific viewpoints. We further demonstrate DAGGER, a targeted adversarial attack directly perturbing 3D Gaussians without access to underlying training data, deceiving multi-stage object detectors e.g., Faster R-CNN, through established methods such as projected gradient descent. These attacks highlight underexplored vulnerabilities in 3DGS, introducing a new potential threat to robotic learning for autonomous navigation and other safety-critical 3DGS applications.

cs.CR↗

arXiv2025

Prescribed-Time Boresight Control of Spacecraft Under Pointing Constraints

This article proposes an integrated boresight guidance and control (IBGC) scheme to address the boresight reorientation problem of spacecraft under temporal and pointing constraints. A $C^1$ continuous, saturated prescribed-time adjustment (PPTA) function is presented, along with the establishment of a practical prescribed-time stability criterion. Utilizing the time scale transformation technique and the PPTA function, we propose a prescribed-time guidance law that guides the boresight vector from almost any initial orientation in free space to a small neighborhood of the goal orientation within a preassigned time, while avoiding all forbidden zones augmented with safety margins. Subsequently, a prescribed-time disturbance observer (PTDO) is derived to reconstruct the external disturbances. By leveraging barrier and PPTA functions, a PTDO-based reduced-attitude tracking controller is developed, which ensures prescribed-time boresight tracking within a ``safe tube''. By judiciously setting the safety margins, settling times, and safe tube for the guidance and control laws, the proposed IBGC scheme achieves pointing-constrained boresight reorientation within a required task completion time. Simulation and experimental results demonstrate the efficacy of the proposed IBGC scheme.

eess.SY↗

arXiv2024

Multi-User Mobile Augmented Reality for Cardiovascular Surgical Planning

Collaborative planning for congenital heart diseases typically involves creating physical heart models through 3D printing, which are then examined by both surgeons and cardiologists. Recent developments in mobile augmented reality (AR) technologies have presented a viable alternative, known for their ease of use and portability. However, there is still a lack of research examining the utilization of multi-user mobile AR environments to support collaborative planning for cardiovascular surgeries. We created ARCollab, an iOS AR app designed for enabling multiple surgeons and cardiologists to interact with a patient's 3D heart model in a shared environment. ARCollab enables surgeons and cardiologists to import heart models, manipulate them through gestures and collaborate with other users, eliminating the need for fabricating physical heart models. Our evaluation of ARCollab's usability and usefulness in enhancing collaboration, conducted with three cardiothoracic surgeons and two cardiologists, marks the first human evaluation of a multi-user mobile AR tool for surgical planning. ARCollab is open-source, available at https://github.com/poloclub/arcollab.

cs.HC↗

arXiv2024

Interactive Visual Learning for Stable Diffusion

Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. It tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations. This integration enables users to fluidly transition between multiple levels of abstraction through animations and interactive elements. Offering real-time hands-on experience, Diffusion Explainer allows users to adjust Stable Diffusion's hyperparameters and prompts without the need for installation or specialized hardware. Accessible via users' web browsers, Diffusion Explainer is making significant strides in democratizing AI education, fostering broader public access. More than 7,200 users spanning 113 countries have used our open-sourced tool at https://poloclub.github.io/diffusion-explainer/. A video demo is available at https://youtu.be/MbkIADZjPnA.

cs.HC↗

arXiv2024

ARCollab: Towards Multi-User Interactive Cardiovascular Surgical Planning in Mobile Augmented Reality

Surgical planning for congenital heart diseases requires a collaborative approach, traditionally involving the 3D-printing of physical heart models for inspection by surgeons and cardiologists. Recent advancements in mobile augmented reality (AR) technologies have offered a promising alternative, noted for their ease-of-use and portability. Despite this progress, there remains a gap in research exploring the use of multi-user mobile AR environments for facilitating collaborative cardiovascular surgical planning. We are developing ARCollab, an iOS AR application designed to allow multiple surgeons and cardiologists to interact with patient-specific 3D heart models in a shared environment. ARCollab allows surgeons and cardiologists to import heart models, perform gestures to manipulate the heart, and collaborate with other users without having to produce a physical heart model. We are excited by the potential for ARCollab to make long-term real-world impact, thanks to the ubiquity of iOS devices that will allow for ARCollab's easy distribution, deployment and adoption.

cs.HC↗

arXiv2023

DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb.

cs.CV↗

arXiv2023

Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion

Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex structures and operations often pose challenges for non-experts to grasp. We present Diffusion Explainer, the first interactive visualization tool that explains how Stable Diffusion transforms text prompts into images. Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex structure with explanations of the underlying operations. By comparing image generation of prompt variants, users can discover the impact of keyword changes on image generation. A 56-participant user study demonstrates that Diffusion Explainer offers substantial learning benefits to non-experts. Our tool has been used by over 10,300 users from 124 countries at https://poloclub.github.io/diffusion-explainer/.

cs.CL↗

arXiv2022

Evaluation of Argo Scholar with Observational Study

Discovering and making sense of relevant literature is fundamental in any scientific field. Node-link diagram-based visualization tools can aid this process; however, existing tools have been evaluated only on small scales. This paper evaluates Argo Scholar, an open-source visualization tool designed for interactive exploration of literature and easy sharing of exploration results. A large-scale user study of 122 participants from diverse backgrounds and experiences showed that Argo Scholar is effective at helping users find related work and understand paper connections, and incremental graph-based exploration is effective across diverse disciplines. Based on the user study and user feedback, we provide design considerations and feature suggestions for future work.

cs.HC↗