The Change You Want to See

Sachdeva, Ragav; Zisserman, Andrew

doi:10.1109/wacv56688.2023.00398

Cited by 9 publications

(2 citation statements)

References 51 publications

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“…and robust to small variations in viewpoint (limited relocalization accuracy). A range of machine learning approaches for this application scenario has been published, such as [41].…”

Section: Robust Change Detectionmentioning

confidence: 99%

First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Waikat,

Jelidi,

Lic

et al. 2024

Energies

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Transformers are a very important asset in the electrical transmission grid, and they can suffer from destructive events—e.g., rare transformer fires. Unfortunately, destructive events often lead to a lack of data available for investigators during post-event forensics and failure analysis. This fact has motivated our design and implementation of a robotic multi-sensor platform and cloud backend solution for the lifecycle monitoring, inspection, diagnostics, and condition assessment of transformers. The robotic platform collects data from specific viewpoints around the transformer during operation and at specific relevant lifecycle milestones of the transformer (e.g., at the factory acceptance test) in an automated, repetitive, precise, and reliable manner. The acquired data are stored in the cloud backend, which also provides computing resources and data access to relevant in- and off-premises services (e.g., respectively, SCADA systems, and weather reports). In this paper, we present the results of our first measurement campaign to showcase the value of our solution for transformer lifecycle monitoring, for anomaly detection, and as a crucial tool for post-event forensics in the case of destructive events.

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“…and robust to small variations in viewpoint (limited relocalization accuracy). A range of machine learning approaches for this application scenario has been published, such as [41].…”

Section: Robust Change Detectionmentioning

confidence: 99%

First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Waikat,

Jelidi,

Lic

et al. 2024

Energies

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“…Moreover, the vast amount of image-text instructions and corresponding driving behavior labels generated from the rich camera images and sensor information in the nuScenes dataset also enhances the interpretability and transparency of the entire decision process. ✓ NuScenes-QA [47] ✓ ✓ Rank2Tell [48] ✓ ✓ BDD-X [49] ✓ ✓ DRAMA [50] ✓ ✓ Ours ✓ ✓ ✓ ✓…”

Section: Dataset Analysismentioning

confidence: 99%

DriveLLaVA: Human-Level Behavior Decisions via Vision Language Model

Zhao,

Yuan,

et al. 2024

Sensors

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Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rules, and analyzing the intentions of other traffic participants. Existing behavior decisions are primarily implemented based on rule-based methods, exhibiting insufficient generalization capabilities when faced with new and unseen driving scenarios. In this paper, we propose a novel behavior decision method that leverages the inherent generalization and commonsense reasoning abilities of visual language models (VLMs) to learn and simulate the behavior decision process in human driving. We constructed a novel instruction-following dataset containing a large number of image–text instructions paired with corresponding driving behavior labels, to support the learning of the Drive Large Language and Vision Assistant (DriveLLaVA) and enhance the transparency and interpretability of the entire decision process. DriveLLaVA is fine-tuned on this dataset using the Low-Rank Adaptation (LoRA) approach, which efficiently optimizes the model parameter count and significantly reduces training costs. We conducted extensive experiments on a large-scale instruction-following dataset, and compared with state-of-the-art methods, DriveLLaVA demonstrated excellent behavior decision performance. DriveLLaVA is capable of handling various complex driving scenarios, showing strong robustness and generalization abilities.

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