兵团
发表于 2025-3-26 22:42:38
,Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds,ouds to facilitate knowledge transfer and propose an innovative hybrid feature augmentation methodology, which enhances the alignment between the 3D feature space and SAM’s feature space, operating at both the scene and instance levels. Our method is evaluated on many widely-recognized datasets and achieves state-of-the-art performance.
Restenosis
发表于 2025-3-27 02:59:20
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constellation
发表于 2025-3-27 09:04:23
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agonist
发表于 2025-3-27 10:42:23
,ShapeLLM: Universal 3D Object Understanding for Embodied Interaction, data and tested on our newly human-curated benchmark, 3D MM-Vet. . and . achieve state-of-the-art performance in 3D geometry understanding and language-unified 3D interaction tasks, such as embodied visual grounding.
ATRIA
发表于 2025-3-27 17:16:41
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forecast
发表于 2025-3-27 20:35:35
https://doi.org/10.1057/9781137462565stion, these methods show advancement in leveraging Large Language Models (LLMs) for complex problem-solving. Despite their potential, existing VP methods generate all code in a single function, which does not fully utilize LLM’s reasoning capacity and the modular adaptability of code. This results
VEIL
发表于 2025-3-27 22:31:39
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不如屎壳郎
发表于 2025-3-28 05:21:15
Ethical Problems in Alternative Medicinecross diverse range of downstream tasks and domains. With the emergence of such powerful models, it has become crucial to effectively leverage their capabilities in tackling challenging vision tasks. On the other hand, only a few works have focused on devising adversarial examples that transfer well
Deference
发表于 2025-3-28 06:41:26
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Nmda-Receptor
发表于 2025-3-28 12:51:09
R. H. Schneider,J. W. Salerno,S. I. Nidich enhancement network that is capable of predicting clean and full measurements from noisy partial observations. We leverage a denoising autoencoder scheme to acquire rich and noise-robust representations in the measurement space. Through this pipeline, our enhancement network is trained to accuratel