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Found 4 papers, 1 papers with code
An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting
In order to fully utilize the useful cues in conversational relations, this study proposes a novel unsupervised dialog topic segmentation method that combines the Utterance Rewriting (UR) technique with an unsupervised learning algorithm to efficiently utilize the useful cues in unlabeled dialogs by rewriting the dialogs in order to recover the co-referents and omitted words.
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
In this paper, we present a novel framework named R2S that leverages the CoD-Chain of Dialogue logic to guide large language models (LLMs) in generating knowledge-intensive multi-turn dialogues for instruction tuning.
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models
To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research.
Arbitrary Motion Style Transfer with Multi-condition Motion Latent Diffusion Model
Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory motion content and motion style.
