In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience clo...
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We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra...
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In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial...
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Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation....
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Hyperspectral cameras face harsh trade-offs between spatial, spectral, and temporal resolution in an inherently low-photon regime. Computational imaging systems break through these trade-offs with compressive sensing,...
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In today’s world, the significance of explainable AI (XAI) is growing in robotics and point cloud applications, as the lack of transparency in decision-making can pose considerable safety risks, particularly in auton...
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The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each pe...
ISBN:
(纸本)9798331314385
The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect. Project page is https://***/zeng-tao/LCGen.
In this paper we present a CNN-based Interface for the control of prosthetic and robotic hand: a CNN visual system is trained with a set of images of daily life object in order to classify and recognize them. Such a c...
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This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium...
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In the field of precision agriculture, plant growth rate monitoring is an important issue to study the influence of soil, weather, or agronomic practices on its growing condition. A plant's growth rate can be eval...
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