Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

兵团

,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

constellation

agonist

,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

forecast

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

不如屎壳郎

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

Nmda-Receptor

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