4 citations · 6 across the 18 of their papers we have counts for
21 papers
Type-Balanced Contextual Learning for Incremental Named Entity Recognition
Duzhen Zhang, Yahan Yu, Xiuyi Chen +2
Incremental Named Entity Recognition (INER) stands as a pivotal task in information extraction, emphasizing the successive identification of new entity types within unstructured te…
VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation
Tianxin Xie, Chenxing Li, Dong Yu +1
Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.…
PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation
Tianxin Xie, Wentao Lei, Kai Jiang +27
Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. P…
AudioRAG+: Feedback-driven Retrieval-augmented Audio Generation with Large Audio Language Models
Junqi Zhao, Chenxing Li, Jinzheng Zhao +4
We propose a general feedback-driven retrieval-augmented generation (RAG) approach that leverages Large Audio Language Models (LALMs) to address the missing or imperfect synthesis…
AudioGenie-Reasoner: A Training-Free Multi-Agent Framework for Coarse-to-Fine Audio Deep Reasoning
Yan Rong, Chenxing Li, Dong Yu +1
Audio deep reasoning is a challenging task that requires expert-level perception, multi-step logical inference, and the integration of contextual knowledge. However, existing model…
Exploring Stability-Plasticity Trade-offs for Continual Named Entity Recognition
Duzhen Zhang, Chenxing Li, Jiahua Dong +2
Continual Named Entity Recognition (CNER) is an evolving field that focuses on sequentially updating an existing model to incorporate new entity types. Previous CNER methods primar…
DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Yisu Liu, Chenxing Li, Wanqian Zhang +6
Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and…
EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering
Tianxin Xie, Shan Yang, Chenxing Li +2
Text-to-speech (TTS) has shown great progress in recent years. However, most existing TTS systems offer only coarse and rigid emotion control, typically via discrete emotion labels…
Mitigating Audiovisual Mismatch in Visual-Guide Audio Captioning
Le Xu, Chenxing Li, Yong Ren +5
Current vision-guided audio captioning systems frequently fail to address audiovisual misalignment in real-world scenarios, such as dubbed content or off-screen sounds. To bridge t…
Hearing from Silence: Reasoning Audio Descriptions from Silent Videos via Vision-Language Model
Yong Ren, Chenxing Li, Le Xu +7
Humans can intuitively infer sounds from silent videos, but whether multimodal large language models can perform modal-mismatch reasoning without accessing target modalities remain…
Information-Theoretic Complementary Prompts for Improved Continual Text Classification
Duzhen Zhang, Yong Ren, Chenxing Li +2
Continual Text Classification (CTC) aims to continuously classify new text data over time while minimizing catastrophic forgetting of previously acquired knowledge. However, existi…
Dopamine Audiobook: A Training-free MLLM Agent for Emotional and Immersive Audiobook Generation
Yan Rong, Shan Yang, Chenxing Li +2
Audiobook generation aims to create rich, immersive listening experiences from multimodal inputs, but current approaches face three critical challenges: (1) the lack of synergistic…
Lifelong Learning of Large Language Model based Agents: A Roadmap
Junhao Zheng, Chengming Shi, Xidi Cai +5
Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously a…
Federated Incremental Named Entity Recognition
Duzhen Zhang, Yahan Yu, Chenxing Li +2
Federated Named Entity Recognition (FNER) boosts model training within each local client by aggregating the model updates of decentralized local clients, without sharing their priv…
SRC-gAudio: Sampling-Rate-Controlled Audio Generation
Chenxing Li, Manjie Xu, Dong Yu
We introduce SRC-gAudio, a novel audio generation model designed to facilitate text-to-audio generation across a wide range of sampling rates within a single model architecture. SR…
Video-to-Audio Generation with Fine-grained Temporal Semantics
Yuchen Hu, Yu Gu, Chenxing Li +2
With recent advances of AIGC, video generation have gained a surge of research interest in both academia and industry (e.g., Sora). However, it remains a challenge to produce tempo…
EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer
Jiarui Hai, Yong Xu, Hao Zhang +4
We introduce EzAudio, a text-to-audio (T2A) generation framework designed to produce high-quality, natural-sounding sound effects. Core designs include: (1) We propose EzAudio-DiT,…
Towards Diverse and Efficient Audio Captioning via Diffusion Models
Manjie Xu, Chenxing Li, Xinyi Tu +4
We introduce Diffusion-based Audio Captioning (DAC), a non-autoregressive diffusion model tailored for diverse and efficient audio captioning. Although existing captioning models r…
STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment
Yong Ren, Chenxing Li, Manjie Xu +4
Visual and auditory perception are two crucial ways humans experience the world. Text-to-video generation has made remarkable progress over the past year, but the absence of harmon…
SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis
Helin Wang, Meng Yu, Jiarui Hai +5
In this paper, we introduce SSR-Speech, a neural codec autoregressive model designed for stable, safe, and robust zero-shot textbased speech editing and text-to-speech synthesis. S…
Video-to-Audio Generation with Hidden Alignment
Manjie Xu, Chenxing Li, Xinyi Tu +5
Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthr…