Shih‐Fang Chen

Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence

Shih‐Fang Chen

At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world. By integrating observations with accumulated experience, the human visual system can continuously adapt to variations in both the target and its …

Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence

Shih‐Fang Chen

At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world. By integrating observations with accumulated experience, the human visual system can continuously adapt to variations in both the target and its …

GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture

Shih‐Fang Chen, Jun-Cheng Chen, I‐Hong Jhuo, Yen-Yu Lin

The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity. In contrast, recent generic object trackers are often optimized for training targets, which limits robustness …

GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing

Shih‐Fang Chen, Jun-Cheng Chen, I‐Hong Jhuo, Yen-Yu Lin

Human perception for effective object tracking in 2D video streams arises from the implicit use of prior 3D knowledge and semantic reasoning. In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the target and its surroundings, while …

GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing

Shih‐Fang Chen, Jun-Cheng Chen, I‐Hong Jhuo, Yen-Yu Lin

Human perception for effective object tracking in 2D video streams arises from the implicit use of prior 3D knowledge and semantic reasoning. In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the target and its surroundings, while …

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Sensory-CoKGE: A contextualized knowledge graph embedding framework using language models for converting text-based food attributes into numerical representation

Yu-Tang Chang, Shih‐Fang Chen

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SparseEB-gMCR: A Generative Solver for Extreme Sparse Components with Application to Contamination Removal in GC-MS

Shih‐Fang Chen

Analytical chemistry instruments provide physically meaningful signals for elucidating analyte composition, and those with high mass or spectral resolution generate signals sparse enough for direct interpretation against chemical libraries. enerative multivariate curve resolution (gMCR) models a sample as the linear superposition of …

EB-gMCR: Energy-Based Generative Modeling for Signal Unmixing and Multivariate Curve Resolution

Yu-Tang Chang, Shih‐Fang Chen

A single measurement of a chemical mixture, a reaction mixture, a natural extract, or a tissue, records the sum of the profiles of the few components it contains, each weighted by its concentration. Recovering the components and their concentrations from a collection …

Nurse practitioner applies extracorporeal shock wave therapy for patients with frozen shoulder: A randomized controlled trial

Sheng‐Shiung Huang, Shih‐Fang Chen, Shiow‐Luan Tsay, Wei‐Tso Chia et autres

BACKGROUND: Nurse practitioners (NPs) have increasingly applied extracorporeal shockwave therapy (ESWT) to treat patients with frozen shoulders in acute care settings. However, the effectiveness of NPs using ESWT in patients has not yet been examined. PURPOSE: To determine the efficacy of NP-led …

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Developing an Image-Text System for Pest and Disease Identification for Vegetable Crops in Urban Community Farming

Ching-Lan Hsu, Tingting Li, Shih‐Fang Chen

Abstract. Urban community farming promotes healthy living, but novice farmers face challenges in pest and disease management. This study develops a multimodal identification system combining image and text analysis for vegetable crop pests and diseases. A total of 2,791 field images and …

Development of TOMATO: A Deep Learning-Based Temporal Observation and Monitoring system for Automated Tomato phenOmics

Ssu-Chi Chen, Zi-Heng Jian, Ya‐Ping Lin, Shih‐Fang Chen

Abstract. Phenotypic evaluation is critical for understanding plant growth characteristics and genetic potential but faces significant human resource limitations in large-scale applications. This study developed a Temporal Observation and Monitoring system for Automated Tomato phenOmics (TOMATO) that integrates with a self-guided field …

Machine Learning-Based Non-Destructive Classification of Tilapia Flesh Using Spectroscopic Techniques

Pin-Wei Chen, Y. H. Chiu, Shih‐Fang Chen

Abstract. Tilapia, the leading aquaculture species in Taiwan, faces quality challenges due to internal flesh abnormalities, such as those caused by Streptococcosis, which are difficult to detect until it is sectioned. This study addresses these challenges by applying hyperspectral imaging (HSI) combined …