5 accepted papers
Despite the remarkable success of semantic token learning in NLP and vision domains, token-level representation mechanisms face fundamental challenges when extended to continuous time series analysis. We identify a core limitation lies in the intrinsic absence of semantically meaningful tokenization
Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state space models (SSMs) like Mamba promise linear complexity, their pixel-serial scanning remains a fundamental bottleneck for
Low-light image enhancement (LLIE) faces persistent challenges in balancing reconstruction fidelity with cross-scenario generalization. While existing methods predominantly focus on deterministic pixel-level mappings between paired low/normal-light images, they often neglect the continuous physical…
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However most of them request supervised pre-training on labelled datasets. This paper proposes an unsupervised kernel estimation model named dynamic kernel prior (DKP) to realize an u…
Support vector machines (SVMs) are one of the most widely used supervised learning algorithms for classification problems. Recent years have witnessed an increasing interest in distributed variants of SVMs, in which the (labeled) training data is distributed across different nodes. While a number of…