Zobrazeno 1 - 10
of 1 531
pro vyhledávání: '"A. Chrysos"'
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation
Supervised deep learning requires massive labeled datasets, but obtaining annotations is not always easy or possible, especially for dense tasks like semantic segmentation. To overcome this issue, numerous works explore Unsupervised Domain Adaptation
Externí odkaz:
http://arxiv.org/abs/2411.18728
Autor:
Xiong, Zheyang, Cai, Ziyang, Cooper, John, Ge, Albert, Papageorgiou, Vasilis, Sifakis, Zack, Giannou, Angeliki, Lin, Ziqian, Yang, Liu, Agarwal, Saurabh, Chrysos, Grigorios G, Oymak, Samet, Lee, Kangwook, Papailiopoulos, Dimitris
Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities. In this study, we explore a surprising phenomenon related to ICL: LLMs can perform multiple, computationally distinct ICL tasks simultaneously, during a
Externí odkaz:
http://arxiv.org/abs/2410.05603
We introduce MIGS (Multi-Identity Gaussian Splatting), a novel method that learns a single neural representation for multiple identities, using only monocular videos. Recent 3D Gaussian Splatting (3DGS) approaches for human avatars require per-identi
Externí odkaz:
http://arxiv.org/abs/2407.07284
Extracting Implicit Neural Representations (INRs) on video data poses unique challenges due to the additional temporal dimension. In the context of videos, INRs have predominantly relied on a frame-only parameterization, which sacrifices the spatiote
Externí odkaz:
http://arxiv.org/abs/2406.19299
Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractiona
Externí odkaz:
http://arxiv.org/abs/2406.18781
EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introduces a novel graph-based residual state update mechanism (REST) for re
Externí odkaz:
http://arxiv.org/abs/2406.16906
Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study D
Externí odkaz:
http://arxiv.org/abs/2405.19201
Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to inval
Externí odkaz:
http://arxiv.org/abs/2405.04346
In this work, we introduce a method that learns a single dynamic neural radiance field (NeRF) from monocular talking face videos of multiple identities. NeRFs have shown remarkable results in modeling the 4D dynamics and appearance of human faces. Ho
Externí odkaz:
http://arxiv.org/abs/2403.19920
Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these
Externí odkaz:
http://arxiv.org/abs/2403.13134