Zobrazeno 1 - 10
of 173
pro vyhledávání: '"duan, Zhihao"'
We study $\mathbb{Z}_N$ one-form center symmetries in four-dimensional gauge theories using the symmetry topological field theory (SymTFT). In this context, the associated TFT in the five-dimensional bulk is the BF model. We revisit its canonical qua
Externí odkaz:
http://arxiv.org/abs/2410.10036
The enhanced Deep Hierarchical Video Compression-DHVC 2.0-has been introduced. This single-model neural video codec operates across a broad range of bitrates, delivering not only superior compression performance to representative methods but also imp
Externí odkaz:
http://arxiv.org/abs/2410.02598
We study particular integrated correlation functions of two superconformal primary operators of the stress tensor multiplet in the presence of a half-BPS line defect labelled by electromagnetic charges $(p,q)$ in $\mathcal{N}=4$ supersymmetric Yang-M
Externí odkaz:
http://arxiv.org/abs/2409.12786
Recent advances in learning-based image compression typically come at the cost of high complexity. Designing computationally efficient architectures remains an open challenge. In this paper, we empirically investigate the impact of different network
Externí odkaz:
http://arxiv.org/abs/2406.10361
Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a pre-defined fixed nu
Externí odkaz:
http://arxiv.org/abs/2404.07507
Feature compression is a promising direction for coding for machines. Existing methods have made substantial progress, but they require designing and training separate neural network models to meet different specifications of compression rate, perfor
Externí odkaz:
http://arxiv.org/abs/2404.00432
Recent studies reveal a significant theoretical link between variational autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to estimate the theoretical upper bound of the information rate-distortion function of images. Such est
Externí odkaz:
http://arxiv.org/abs/2403.18535
Image compression emerges as a pivotal tool in the efficient handling and transmission of digital images. Its ability to substantially reduce file size not only facilitates enhanced data storage capacity but also potentially brings advantages to the
Externí odkaz:
http://arxiv.org/abs/2403.06288
This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g., adapting to new data or target bitrates) without breaking backward compatibility, the ability to decode bitstreams encoded by the original
Externí odkaz:
http://arxiv.org/abs/2402.18862
While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and lo
Externí odkaz:
http://arxiv.org/abs/2401.11615