activity
20242026
most citedUniversal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations

5 citations · 7 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

EvoState: Closed-Loop Visual State Management for Long-Form Video Generation

Xinlei Yin, Xiulian Peng, Xiao Li +2

Multi-shot long-form video generation remains challenging due to identity drift and compounding inconsistencies across shots. Existing methods encode past shots as compressed conte…

cs.CV2026

From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations

Yuchen Guan, Xiao Li, Zongyu Guo +4

We propose a new paradigm for long video understanding by treating a long video as a Neural Knowledge Representation (NKR). NKR represents video contents neither as a stream of tok…

cs.CV2026

Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search

Xinlei Yin, Xiulian Peng, Xiao Li +2

Long video understanding presents significant challenges for vision-language models due to extremely long context windows. Existing solutions relying on naive chunking strategies w…

cs.CV2025

Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video

Xiao Li, Qi Chen, Xiulian Peng +3

We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with les…

cs.SD2025

Text-Queried Audio Source Separation via Hierarchical Modeling

Xinlei Yin, Xiulian Peng, Xue Jiang +2

Target audio source separation with natural language queries presents a promising paradigm for extracting arbitrary audio events through arbitrary text descriptions. Existing metho…

cs.SD2025★ 5 cited

Universal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations

Xue Jiang, Xiulian Peng, Yuan Zhang +1

Current large speech language models are mainly based on semantic tokens from discretization of self-supervised learned representations and acoustic tokens from a neural codec, fol…

cs.SD2024★ 2 cited

Convert and Speak: Zero-shot Accent Conversion with Minimum Supervision

Zhijun Jia, Huaying Xue, Xiulian Peng +1

Low resource of parallel data is the key challenge of accent conversion(AC) problem in which both the pronunciation units and prosody pattern need to be converted. We propose a two…