Author name cluster

Zhenyu Yu

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9 papers

2 author rows

AAAI Conference 2026 Short Paper

DINOv3-Powered Multi-Task Foundation Model for Quantitative Remote Sensing Estimation (Student Abstract)

Zhenyu Yu
Mohd Yamani Idna Idris
Pei Wang
Rizwan Qureshi

Quantitative remote sensing estimation is critical for environmental monitoring, providing continuous measures of vegetation indices, canopy height, and carbon stock. Traditional radiative-transfer models and empirical regressions require expert knowledge and generalize poorly, while deep learning methods remain task-specific. We propose SatelliteCalculator+, a DINOv3-powered multi-task foundation model for continuous regression of spectral and structural variables. The framework combines prompt-driven cross-attentive adapters with lightweight MLP decoders, enabling efficient dense prediction from frozen features. To overcome limited supervision, we synthesize over one million paired samples from SPOT 6/7 imagery using physically defined formulas. On the Open-Canopy dataset, SatelliteCalculator+ achieves competitive accuracy across eight ecological variables while reducing inference cost, demonstrating the promise of self-supervised transformers and scalable multi-task learning for large-scale Earth observation.

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EAAI Journal 2025 Journal Article

ForgetMe: Benchmarking the selective forgetting capabilities of generative models

Zhenyu Yu
Mohd Yamani Idna Idris
Pei Wang
Yuelong Xia
Yong Xiang

The widespread adoption of diffusion models in image generation has increased the demand for privacy-compliant unlearning. However, due to the high-dimensional nature and complex feature representations of diffusion models, achieving selective unlearning remains challenging, as existing methods struggle to remove sensitive information while preserving the consistency of non-sensitive regions. To address this, we propose an Automatic Dataset Creation Framework based on prompt-based layered editing and training-free local feature removal, constructing the ForgetMe dataset and introducing the Entangled evaluation metric. The Entangled metric quantifies unlearning effectiveness by assessing the similarity and consistency between the target and background regions and supports both paired (Entangled-D) and unpaired (Entangled-S) image data, enabling unsupervised evaluation. The ForgetMe dataset encompasses a diverse set of real and synthetic scenarios, including CUB-200-2011 (Birds), Stanford-Dogs, ImageNet, and a synthetic cat dataset. We apply LoRA fine-tuning on Stable Diffusion to achieve selective unlearning on this dataset and validate the effectiveness of both the ForgetMe dataset and the Entangled metric, establishing them as benchmarks for selective unlearning. Our work provides a scalable and adaptable solution for advancing privacy-preserving generative AI. Code is available at: https: //github. com/YuZhenyuLindy/ForgetMe.

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AAAI Conference 2025 Conference Paper

Yuan: Yielding Unblemished Aesthetics Through a Unified Network for Visual Imperfections Removal in Generated Images

Zhenyu Yu
Chee Seng Chan

Generative AI presents transformative potential across various domains, from creative arts to scientific visualization. However, the utility of AI-generated imagery is often compromised by visual flaws, including anatomical inaccuracies, improper object placements, and misplaced textual elements. These imperfections pose significant challenges for practical applications. To overcome these limitations, we introduce Yuan, a novel framework that autonomously corrects visual imperfections in text-to-image synthesis. Yuan uniquely conditions on both the textual prompt and the segmented image, generating precise masks that identify areas in need of refinement without requiring manual intervention—a common constraint in previous methodologies. Following the automated masking process, an advanced inpainting module seamlessly integrates contextually coherent content into the identified regions, preserving the integrity and fidelity of the original image and associated text prompts. Through extensive experimentation on publicly available datasets such as ImageNet100 and Stanford Dogs, along with a custom-generated dataset, Yuan demonstrated superior performance in eliminating visual imperfections. Our approach consistently achieved higher scores in quantitative metrics, including NIQE, BRISQUE, and PI, alongside favorable qualitative evaluations. These results underscore Yuan's potential to significantly enhance the quality and applicability of AI-generated images across diverse fields.

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ICRA Conference 2009 Conference Paper

Combined yaw and roll control of an autonomous boat

Xinping Bao
Kenzo Nonami
Zhenyu Yu

In this paper we try to develop a host-based system and study actual sea trials via rudder based roll control method. To authors' best knowledge, the boat we investigated is the smallest among those reported in the literature. An autonomous boat model is obtained by a system identification approach. The identified system is designed with frequency-shaped sliding mode control. The control scheme is composed of a sliding mode observer and a sliding mode controller. The stability and reachability of the switching function are proved by Lyapunov theory. Computer simulations and experiment show that successful course keeping and roll reduction results are achieved.

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ICRA Conference 2009 Conference Paper

Embedded autopilot for accurate waypoint navigation and trajectory tracking: Application to miniature rotorcraft UAVs

Farid Kendoul
Zhenyu Yu
Kenzo Nonami

In this paper, we describe a miniature flight platform weighing less than 700 grams and capable of waypoint navigation, trajectory tracking, precise hovering and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini and micro rotorcraft, a lightweight/portable and inexpensive Guidance, Navigation, and Control system (GN&C) was developed. To compensate for the weaknesses of the low-cost equipment, we put our efforts in obtaining a reliable model-based nonlinear controller. The GN&sC system was implemented on a small four rotor helicopter which has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Flight test results that demonstrate the operation of the GN&C algorithms on a real MAV are presented.

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IROS Conference 2006 Conference Paper

Development of 3D Vision Enabled Small-scale Autonomous Helicopter

Zhenyu Yu
Demian Celestino
Kenzo Nonami

This paper presents the development of vision based autonomous helicopter at Chiba University. The platform is designed to enable autonomous flight of a small-scale helicopter in unstructured environment. 3D vision system is chosen for the perception and measurement purpose. The whole system is designed with the consideration of payload, hardware availability, and software performance. The hardware is built with COTS (commercially off the shelf) products. The core control software is built on RTLinux for its real time performance. Based on the developed platform, we have studied the application of vision system for landing control. The experimental results are shown in the paper

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ICRA Conference 1996 Conference Paper

Calibration free visually controlled manipulation of parts in a robotic manufacturing workcell

Bijoy K. Ghosh
Tzyh Jong Tarn
Ning Xi 0001
Zhenyu Yu
Di Xiao

In this paper we introduce a new approach to visually manipulate, with the aid of a robot manipulator, a part placed randomly on a rotating turntable. The highlight of our approach is that the camera and the robot end effector are both assumed to be uncalibrated. We only assume that the height of the robot end effector is known. Our approach utilizes virtual rotation of the camera via image processing not previously introduced in the literature. Finally, the tracking scheme is implemented by planning the error and gradually forcing it to zero while maintaining the torque controls within acceptable limits. This way we demonstrate a new visually guided analytical tracking scheme.

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IROS Conference 1995 Conference Paper

Multi-sensor based planning and control for robotic manufacturing systems

Zhenyu Yu
Bijoy K. Ghosh
Ning Xi 0001
Tzyh Jong Tarn

A multi-sensor based planning and control scheme for robotic manufacturing is presented in this paper. The proposed approach fuses sensory information from various sensors at different temporal and spatial scales in an event-based planning and control scheme. By combining the measurement of an encoder sensor, relative spatial information obtained from processing of visual measurement is mapped to the absolute task-space of the robot, delayed data obtained from a displacement based vision algorithm that represent absolute part position measurement is brought to up to date. A four-step approach to planning and control of a robotic manipulator is discussed. An event-driven tracking and control scheme that is based on multi-sensor information is given. The approach is illustrated by considering a manufacturing workcell where the manipulator is commanded to pick up a part on a disc conveyor under the guidance of computer vision.

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ICRA Conference 1995 Conference Paper

Temporal and Spartial Sensor Fusion in a Robotic Manufacturing Workcell

Zhenyu Yu
Bijoy K. Ghosh
Ning Xi 0001
Tzyh Jong Tarn

Discusses the problem of using visual and other sensors in the manipulation of a part by a robotic manipulator in a manufacturing workcell. The authors' emphasis is on the part localization problem involved. The authors introduce a new sensor-fusion approach which fuses sensory information from different sensors at various spatial and temporal scales. Relative spatial information obtained from processing of visual information is mapped to absolute taskspace of the robot through fusing of information from an encoder. Data obtained this way can be superimposed upon data obtained from displacement based vision algorithms at coarser time scales to improve overall reliability. Tracking plans reflecting sensor fusion are proposed. The localization of a part by spatial sensor fusion is experimentally demonstrated to be able to give required fast and accurate part localization.

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