Jinchang Zhang

When Do Learned Priors Help Visual Inertial Estimation? A Controlled Study of Prior Integration, Calibration, Initialization, and Backend Consistency

Jinchang Zhang, Guoyu Lu

Learned components are increasingly integrated into geometric visual--inertial estimators to provide motion, depth, bias, uncertainty, or confidence cues. Yet it remains unclear whether gains arise from useful learned priors or from changes in the backend, calibration, initialization, temporal association, or evaluation gauge. …

When Do Learned Priors Help Visual Inertial Estimation? A Controlled Study of Prior Integration, Calibration, Initialization, and Backend Consistency

Jinchang Zhang, Guoyu Lu

Learned components are increasingly integrated into geometric visual--inertial estimators to provide motion, depth, bias, uncertainty, or confidence cues. Yet it remains unclear whether gains arise from useful learned priors or from changes in the backend, calibration, initialization, temporal association, or evaluation gauge. …

Foundation-Assisted Active Learning for Object Detection Annotation

Jinchang Zhang, Arnold Zumbrun, Jing Lin, Guoyu Lu

The annotation cost for remote sensing object detection is high, while existing active learning methods still face several challenges in object detection scenarios, including the coupling of localization and classification uncertainty, severe localization noise in the cold-start stage, and pseudo-diversity caused by …

Foundation-Assisted Active Learning for Object Detection Annotation

Jinchang Zhang, Arnold Zumbrun, Jing Lin, Guoyu Lu

The annotation cost for remote sensing object detection is high, while existing active learning methods still face several challenges in object detection scenarios, including the coupling of localization and classification uncertainty, severe localization noise in the cold-start stage, and pseudo-diversity caused by …

Fractal Autoregressive Depth Estimation with Continuous Token Diffusion

Jinchang Zhang, Xinrou Kang, Guoyu Lu

Monocular depth estimation can benefit from autoregressive (AR) generation, but direct AR modeling is hindered by the modality gap between RGB and depth, inefficient pixel-wise generation, and instability in continuous depth prediction. We propose a Fractal Visual Autoregressive Diffusion framework that reformulates …

Fractal Autoregressive Depth Estimation with Continuous Token Diffusion

Jinchang Zhang, Xinrou Kang, Guoyu Lu

Monocular depth estimation can benefit from autoregressive (AR) generation, but direct AR modeling is hindered by the modality gap between RGB and depth, inefficient pixel-wise generation, and instability in continuous depth prediction. We propose a Fractal Visual Autoregressive Diffusion framework that reformulates …

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3D Plant Root Skeleton Detection and Extraction

Jiakai Lin, Jinchang Zhang, Ge Jin, Wen‐Zhan Song et autres

Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult …

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Automated Genomic Interpretation via Concept Bottleneck Models for Medical Robotics

Z.G. Li, Jinchang Zhang, Ming Zhang, Guoyu Lu

We propose an automated genomic interpretation module that transforms raw DNA sequences into actionable, interpretable decisions suitable for integration into medical automation and robotic systems. Our framework combines Chaos Game Representation (CGR) with a Concept Bottleneck Model (CBM), enforcing predictions to flow …

Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillation

Jinchang Zhang, Zijun Li, Jiakai Lin, Guoyu Lu

Event cameras offer advantages in object detection tasks due to high-speed response, low latency, and robustness to motion blur. However, event cameras lack texture and color information, making open-vocabulary detection particularly challenging. Current event-based detection methods are typically trained on predefined categories, …

Graph Integrated Multimodal Concept Bottleneck Model

Jiakai Lin, Jinchang Zhang, Guoyu Lu

With growing demand for interpretability in deep learning, especially in high stakes domains, Concept Bottleneck Models (CBMs) address this by inserting human understandable concepts into the prediction pipeline, but they are generally single modal and ignore structured concept relationships. To overcome these …

3D Plant Root Skeleton Detection and Extraction

Jiakai Lin, Jinchang Zhang, Ge Jin, Wen‐Zhan Song et autres

Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult …

Tracking Poultry Drinking Behavior and Floor Eggs in Cage-Free Houses with Innovative Depth Anything Model

Xiao Yan Yang, Guoyu Lu, Jinchang Zhang, Bidur Paneru et autres

In recent years, artificial intelligence (AI) has significantly impacted agricultural operations, particularly with the development of deep learning models for animal monitoring and farming automation. This study focuses on evaluating the Depth Anything Model (DAM), a cutting-edge monocular depth estimation model, for …

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