6 accepted papers
Aligning natural language descriptions with precise 3D human poses remains a big challenge due to the scarcity of effective pose representation mechanisms and large-scale, semantically rich datasets. To overcome these limitations, we first introduce **CLEP-2M**, the largest 3D pose-language dataset
Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models (PRMs) focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of in
Recovering 3D human motion from monocular videos in-the-wild remains challenging due to occlusions, rapid movements, and viewpoint variations. To address these challenges, we introduce **Recover-Anyone Module (RAM)**, a unified framework for real-time and accurate 3D human motion reconstruction. RAM
Recent studies have demonstrated that supervised fine-tuning of Large Language Models (LLMs) can significantly enhance performance across various Information Extraction (IE) tasks. However, the critical IE task of Relation Extraction (RE) faces substantial cost barriers in the supervised fine-tuning…
Chinese Spelling Correction (CSC) is a challenging and essential task in natural language processing. In this study, we introduces a new method for Chinese Spelling Correction (CSC) that addresses three unattended areas in prior studies. Firstly, we use an Implicit Knowledge Extraction Network to ov…
Word segmentation task is the cornerstone of text processing. There are 7111 languages worldwide, most of which are low-resource languages. This paper attempts to solve the problem of multilingual unsupervised word segmentation using common points between languages without tagged corpus. We find tha…