arXiv subjects

Guohao Lan

Publications and source records attributed to Guohao Lan.

14 recordsLinked to original sources

arXiv2026

How Much Does It Cost to Answer My Question? Benchmarking Cloud VLM-based VQA Systems

Vision-language models (VLMs) are becoming a practical backend for mobile visual question answering (VQA) systems, enabling smartphones and smart glasses to answer users' questions about the physical world. Since modern VLMs remain difficult to run on mobile and edge devices, VQA systems increasingly offload inference to cloud-based VLMs. This gives mobile devices access to stronger computation, but it also makes visual input preparation a key system variable: how the image is prepared before offloading affects not only answer quality but also payload size, token cost, and system latency. Proprietary APIs expose little control over model internals or serving behavior, leaving client-side preprocessing as the main practical optimization space for downstream developers. Many such techniques have been proposed for visual offloading, yet their cost-quality impact on commercial cloud VLMs has never been studied. To fill this gap, we present VQABench, the first systematic benchmark that treats client-side input preprocessing as a controlled variable for cloud-VLM-based VQA. We evaluate 12 preprocessing techniques across three VQA datasets and four commercial VLMs from three providers, totaling 95,168 API calls. Our results show that preprocessing is not universally beneficial: its effectiveness depends on the target model, API paradigm, provider token-accounting rule, and task formulation. A poorly selected preprocessing strategy can increase deployment cost or latency while degrading answer accuracy. Overall, our benchmark clarifies when preprocessing helps, when it fails, and why, providing insights to guide future research and real-world deployment of VQA systems.

cs.CV↗

arXiv2026

Talk to Me, Not the Slides: A Real-Time Wearable Assistant for Improving Eye Contact in Presentations

Effective eye contact is a cornerstone of successful public speaking. It strengthens the speaker's credibility and fosters audience engagement. Yet, managing effective eye contact is a skill that demands extensive training and practice, often posing a significant challenge for novice speakers. In this paper, we present SpeakAssis, the first real-time, in-situ wearable system designed to actively assist speakers in maintaining effective eye contact during live presentations. Leveraging a head-mounted eye tracker for gaze and scene view capture, SpeakAssis continuously monitors and analyzes the speaker's gaze distribution across audience and non-audience regions. When ineffective eye-contact patterns are detected, such as insufficient eye contact, or neglect of certain audience segments, SpeakAssis provides timely, context-aware audio prompts via an earphone to guide the speaker's gaze behavior. We evaluate SpeakAssis through a user study involving eight speakers and 24 audience members. Quantitative results show that SpeakAssis increases speakers' eye-contact duration by 62.5% on average and promotes a more balanced distribution of visual attention. Additionally, statistical analysis based on audience surveys reveals that improvements in speaker's eye-contact behavior significantly enhance the audience's perceived engagement and interactivity during presentations.

cs.HC↗

arXiv2025

SecureGaze: Defending Gaze Estimation Against Backdoor Attacks

Gaze estimation models are widely used in applications such as driver attention monitoring and human-computer interaction. While many methods for gaze estimation exist, they rely heavily on data-hungry deep learning to achieve high performance. This reliance often forces practitioners to harvest training data from unverified public datasets, outsource model training, or rely on pre-trained models. However, such practices expose gaze estimation models to backdoor attacks. In such attacks, adversaries inject backdoor triggers by poisoning the training data, creating a backdoor vulnerability: the model performs normally with benign inputs, but produces manipulated gaze directions when a specific trigger is present. This compromises the security of many gaze-based applications, such as causing the model to fail in tracking the driver's attention. To date, there is no defense that addresses backdoor attacks on gaze estimation models. In response, we introduce SecureGaze, the first solution designed to protect gaze estimation models from such attacks. Unlike classification models, defending gaze estimation poses unique challenges due to its continuous output space and globally activated backdoor behavior. By identifying distinctive characteristics of backdoored gaze estimation models, we develop a novel and effective approach to reverse-engineer the trigger function for reliable backdoor detection. Extensive evaluations in both digital and physical worlds demonstrate that SecureGaze effectively counters a range of backdoor attacks and outperforms seven state-of-the-art defenses adapted from classification models.

cs.CV↗

arXiv2024

Defending Deep Regression Models against Backdoor Attacks

Deep regression models are used in a wide variety of safety-critical applications, but are vulnerable to backdoor attacks. Although many defenses have been proposed for classification models, they are ineffective as they do not consider the uniqueness of regression models. First, the outputs of regression models are continuous values instead of discretized labels. Thus, the potential infected target of a backdoored regression model has infinite possibilities, which makes it impossible to be determined by existing defenses. Second, the backdoor behavior of backdoored deep regression models is triggered by the activation values of all the neurons in the feature space, which makes it difficult to be detected and mitigated using existing defenses. To resolve these problems, we propose DRMGuard, the first defense to identify if a deep regression model in the image domain is backdoored or not. DRMGuard formulates the optimization problem for reverse engineering based on the unique output-space and feature-space characteristics of backdoored deep regression models. We conduct extensive evaluations on two regression tasks and four datasets. The results show that DRMGuard can consistently defend against various backdoor attacks. We also generalize four state-of-the-art defenses designed for classifiers to regression models, and compare DRMGuard with them. The results show that DRMGuard significantly outperforms all those defenses.

cs.LG↗

arXiv2024

Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting

Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption by 98.7% and harvesting up to 4.5mW of power from RF energy.

cs.NI↗

arXiv2024

PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services

Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users' privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users' private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.

cs.CV↗

arXiv2024

Leveraging Foundation Models for Zero-Shot IoT Sensing

Deep learning models are increasingly deployed on edge Internet of Things (IoT) devices. However, these models typically operate under supervised conditions and fail to recognize unseen classes different from training. To address this, zero-shot learning (ZSL) aims to classify data of unseen classes with the help of semantic information. Foundation models (FMs) trained on web-scale data have shown impressive ZSL capability in natural language processing and visual understanding. However, leveraging FMs' generalized knowledge for zero-shot IoT sensing using signals such as mmWave, IMU, and Wi-Fi has not been fully investigated. In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM's text encoder for zero-shot IoT sensing. To utilize the physics principles governing the generation of IoT sensor signals to derive more effective prompts for semantic embedding extraction, we propose to use cross-attention to combine a learnable soft prompt that is optimized automatically on training data and an auxiliary hard prompt that encodes domain knowledge of the IoT sensing task. To address the problem of IoT embeddings biasing to seen classes due to the lack of unseen class data during training, we propose using data augmentation to synthesize unseen class IoT data for fine-tuning the IoT feature extractor and embedding projector. We evaluate our approach on multiple IoT sensing tasks. Results show that our approach achieves superior open-set detection and generalized zero-shot learning performance compared with various baselines. Our code is available at https://github.com/schrodingho/FM\_ZSL\_IoT.

cs.AI↗

arXiv2023

Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition

Appearance-based gaze estimation has shown great promise in many applications by using a single general-purpose camera as the input device. However, its success is highly depending on the availability of large-scale well-annotated gaze datasets, which are sparse and expensive to collect. To alleviate this challenge we propose ConGaze, a contrastive learning-based framework that leverages unlabeled facial images to learn generic gaze-aware representations across subjects in an unsupervised way. Specifically, we introduce the gaze-specific data augmentation to preserve the gaze-semantic features and maintain the gaze consistency, which are proven to be crucial for effective contrastive gaze representation learning. Moreover, we devise a novel subject-conditional projection module that encourages a share feature extractor to learn gaze-aware and generic representations. Our experiments on three public gaze estimation datasets show that ConGaze outperforms existing unsupervised learning solutions by 6.7% to 22.5%; and achieves 15.1% to 24.6% improvement over its supervised learning-based counterpart in cross-dataset evaluations.

cs.CV↗

arXiv2022

FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning

Gaze estimation is of great importance to many scientific fields and daily applications, ranging from fundamental research in cognitive psychology to attention-aware mobile systems. While recent advancements in deep learning have yielded remarkable successes in building highly accurate gaze estimation systems, the associated high computational cost and the reliance on large-scale labeled gaze data for supervised learning place challenges on the practical use of existing solutions. To move beyond these limitations, we present FreeGaze, a resource-efficient framework for unsupervised gaze representation learning. FreeGaze incorporates the frequency domain gaze estimation and the contrastive gaze representation learning in its design. The former significantly alleviates the computational burden in both system calibration and gaze estimation, and dramatically reduces the system latency; while the latter overcomes the data labeling hurdle of existing supervised learning-based counterparts, and ensures efficient gaze representation learning in the absence of gaze label. Our evaluation on two gaze estimation datasets shows that FreeGaze can achieve comparable gaze estimation accuracy with existing supervised learning-based approach, while enabling up to 6.81 and 1.67 times speedup in system calibration and gaze estimation, respectively.

cs.CV↗

arXiv2020

Simultaneous Energy Harvesting and Gait Recognition using Piezoelectric Energy Harvester

Piezoelectric energy harvester, which generates electricity from stress or vibrations, is gaining increasing attention as a viable solution to extend battery life in wearables. Recent research further reveals that, besides generating energy, PEH can also serve as a passive sensor to detect human gait power-efficiently because its stress or vibration patterns are significantly influenced by the gait. However, as PEHs are not designed for precise measurement of motion, achievable gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. To classify gait reliably while simultaneously storing generated energy, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signal. Second, we propose a long short-term memory (LSTM) network-based classifier to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12% higher recall and harvests up to 127% more energy while consuming 38% less power compared to the state-of-the-art.

eess.SP↗

arXiv2019

Sensing, Computing, and Communication for Energy Harvesting IoTs: A Survey

With the growing number of deployments of Internet of Things (IoT) infrastructure for a wide variety of applications, the battery maintenance has become a major limitation for the sustainability of such infrastructure. To overcome this problem, energy harvesting offers a viable alternative to autonomously power IoT devices, resulting in a number of battery-less energy harvesting IoTs (or EH-IoTs) appearing in the market in recent years. Standards activities are also underway, which involve wireless protocol design suitable for EH-IoTs as well as testing procedures for various energy harvesting methods. Despite the early commercial and standards activities, IoT sensing, computing and communications under unpredictable power supply still face significant research challenges. This paper systematically surveys recent advances in EH-IoTs from several perspectives. First, it reviews the recent commercial developments for EH-IoT in terms of both products and services, followed by initial standards activities in this space. Then it surveys methods that enable the use of energy harvesting hardware as a proxy for conventional sensors to detect contexts in energy efficient manner. Next it reviews the advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices. We also survey recent research in novel wireless communication techniques for EH-IoTs, such as the applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions, and packet-less IoT communications and backscatter communication techniques for energy impoverished environments. The paper is concluded with a discussion of future research directions.

eess.SP↗

arXiv2019

EnTrans:Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

cs.HC↗

arXiv2018

SolarGest: Ubiquitous and Battery-free Gesture Recognition using Solar Cells

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.

cs.HC↗

arXiv2018

Capacitor Based Activity Sensing for Kinetic Powered Wearable IoTs

We propose a novel use of the conventional energy storage component, i.e., capacitor, in kinetic-powered wearable IoTs as a sensor to detect human activities. Since different activities accumulate energies in the capacitor at different rates, these activities can be detected directly by observing the charging rate of the capacitor. The key advantage of the proposed capacitor based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal during the activity detection period thus significantly saving power consumption of the wearable device. A challenge we face is that capacitors are inherently non-linear energy accumulators, which, even for the same activity, leads to significant variations in charging rates at different times depending on the current charge level of the capacitor. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate on charging cycles that are approximately linear. We design and implement a kinetic-powered shoe sole and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 73% less system power compared to conventional motion signal based activity detection.

cs.HC↗