Zobrazeno 1 - 10
of 614 054
pro vyhledávání: '"A-In Ju"'
DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo
Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effect
Externí odkaz:
http://arxiv.org/abs/2412.05268
While perturbation theories constitute a significant foundation of modern quantum system analysis, extending them from the Hermitian to the non-Hermitian regime remains a non-trivial task. In this work, we generalize the Rayleigh-Schr\"odinger pertur
Externí odkaz:
http://arxiv.org/abs/2412.05166
Autor:
Lu, Yen-Ju, Liu, Jing, Thebaud, Thomas, Moro-Velazquez, Laureano, Rastrow, Ariya, Dehak, Najim, Villalba, Jesus
We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SS
Externí odkaz:
http://arxiv.org/abs/2412.04425
Autor:
Zheng, Qi, Fan, Yibo, Huang, Leilei, Zhu, Tianyu, Liu, Jiaming, Hao, Zhijian, Xing, Shuo, Chen, Chia-Ju, Min, Xiongkuo, Bovik, Alan C., Tu, Zhengzhong
Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics,
Externí odkaz:
http://arxiv.org/abs/2412.04508
Autor:
Ming, Ruibo, Wu, Jingwei, Huang, Zhewei, Ju, Zhuoxuan, HU, Jianming, Peng, Lihui, Zhou, Shuchang
Recent advances in auto-regressive large language models (LLMs) have shown their potential in generating high-quality text, inspiring researchers to apply them to image and video generation. This paper explores the application of LLMs to video contin
Externí odkaz:
http://arxiv.org/abs/2412.03758
Young and forming planetesimals experience impacts from particles present in a protostellar disk. Using crater scaling laws, we integrate ejecta distributions for oblique impacts. For impacts at 10 to 65 m/s, expected for impacts associated with a di
Externí odkaz:
http://arxiv.org/abs/2412.03533
Autor:
Xu, Haidong, Zhang, Meishan, Ju, Hao, Zheng, Zhedong, Zhu, Hongyuan, Cambria, Erik, Zhang, Min, Fei, Hao
Producing emotionally dynamic 3D facial avatars with text derived from spoken words (Emo3D) has been a pivotal research topic in 3D avatar generation. While progress has been made in general-purpose 3D avatar generation, the exploration of generating
Externí odkaz:
http://arxiv.org/abs/2412.02508
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training da
Externí odkaz:
http://arxiv.org/abs/2412.02186
The interplay between topology and magnetism often triggers the exotic quantum phases. Here, we report an accessible scheme to engineer the robust $\mathbb{Z}_{2}$ topology by intrinsic magnetism, originating from the zigzag segment connecting two ar
Externí odkaz:
http://arxiv.org/abs/2412.00859
Traditional methods for evaluating the robustness of large language models (LLMs) often rely on standardized benchmarks, which can escalate costs and limit evaluations across varied domains. This paper introduces a novel framework designed to autonom
Externí odkaz:
http://arxiv.org/abs/2412.00765