1 citations · 1 across the 12 of their papers we have counts for
16 papers
TVPara: Paralinguistic Captioning of Expressive TV-Series Speech in Complex Acoustic Environments
Ziyang Zhang, Wenyi Yu, Wen Wu +2
Large audio-language models (LALMs) struggle to parse paralinguistics (e.g., emotion, pace, pitch) in speech that combines intense emotion with complex acoustic environments. Stren…
MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision
Ziyang Zhang, Shenyi Li, Yilin Wang +4
Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived from the same signal. This l…
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors
Boya Zhang, Xilin Dai, Yilin Wang +4
Time Series Foundation Models (TSFMs) provide general-purpose forecasting capabilities, yet their forecasts can remain misaligned with the temporal characteristics of a target doma…
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
MMAG: A Multi-Control Mixed Audio Generation Benchmark
Zihao Zheng, Xuenan Xu, Jiahao Mei +5
Recent audio generation systems have progressed from single-modality synthesis to generating complex acoustic scenes containing speech, music, and sound effects. Therefore, evaluat…
SALMONN-2: Advancing General-Purpose Hearing Abilities with Self-Supervised Representations
Xiaoyu Yang, Xuenan Xu, Wenyi Yu +10
Recent audio large language models (ALLMs) are typically built upon audio encoders trained with large amounts of supervised data. Since self-supervised learning (SSL) audio encoder…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models
Changli Tang, Shurui Li, Junliang Wang +8
Deciphering brain function through non-invasive recordings requires synthesizing complementary high-frequency electromagnetic (EEG/MEG) and low-frequency metabolic (fMRI) signals.…
HoliAntiSpoof: Audio LLM for Holistic Speech Anti-Spoofing
Xuenan Xu, Yiming Ren, Liwei Liu +5
Recent advances in speech synthesis and editing have made speech spoofing increasingly challenging. However, most existing methods treat spoofing as binary classification, overlook…
MMEDIT: A Unified Framework for Multi-Type Audio Editing via Audio Language Model
Ye Tao, Wen Wu, Chao Zhang +3
Text-guided audio editing aims to modify specific acoustic events while strictly preserving non-target content. Despite recent progress, existing approaches remain fundamentally li…
HIPPO: Accelerating Video Large Language Models Inference via Holistic-aware Parallel Speculative Decoding
Qitan Lv, Tianyu Liu, Wen Wu +4
Speculative decoding (SD) has emerged as a promising approach to accelerate LLM inference without sacrificing output quality. Existing SD methods tailored for video-LLMs primarily…
PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Zihao Zheng, Zeyu Xie, Xuenan Xu +3
While recent work in controllable text-to-audio (TTA) generation has achieved fine-grained control through timestamp conditioning, its scope remains limited by audio quality and in…
SPEAR: A Unified SSL Framework for Learning Speech and Audio Representations
Xiaoyu Yang, Yifan Yang, Zengrui Jin +5
Self-supervised learning (SSL) has significantly advanced acoustic representation learning. However, most existing models are optimised for either speech or audio event understandi…
Bayesian Speech Synthesizers Can Learn from Multiple Teachers
Ziyang Zhang, Yifan Gao, Xuenan Xu +3
Text-to-speech (TTS) is a "one-to-many" problem: one text can be spoken in many valid ways, so its realizations form a distribution whose spread depends on the input. Most TTS mode…
SciTS: Scientific Time Series Understanding and Generation with LLMs
Wen Wu, Ziyang Zhang, Liwei Liu +12
The scientific reasoning ability of large language models (LLMs) has recently attracted significant attention. Time series, as a fundamental modality in scientific data, presents u…
UniFlow-Audio: Unified Flow Matching for Audio Generation from Omni-Modalities
Xuenan Xu, Jiahao Mei, Zihao Zheng +9
Audio generation, including speech, music and sound effects, has advanced rapidly in recent years. These tasks can be divided into two categories: time-aligned (TA) tasks, where ea…