2 citations · 3 across the 15 of their papers we have counts for
17 papers
StepAudio 3 Realtime Technical Report
Bin Lin, Bo Zhao, Boyang Zhang +87
Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a…
StepAudio 3 Gen Technical Report
Bin Lin, Bo Zhao, Boyang Wang +68
We introduce StepAudio 3 Gen, a general-purpose audio generation model that supports zero-shot text-to-speech (TTS), voice design, vocal generation, sound effects, music, vibe spee…
StepAudio 2.5 Technical Report
Bin Lin, Bo Zhao, Boyong Wu +98
Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…
Surface-Form Neural Sparse Retrieval: Robust Fuzzy Matching for Industrial Music Search
Paul Greyson, Zhichao Geng, Wei Zhang +1
Music search at the scale of Amazon Music presents a unique challenge: queries frequently deviate from indexed metadata due to misspellings, transpositions, and phonetic variations…
MASRA: MLLM-Assisted Semantic-Relational Consistent Alignment for Video Temporal Grounding
Ran Ran, Jiwei Wei, Shuchang Zhou +5
Video Temporal Grounding (VTG) faces a cross-modal semantic gap that often leads to background features being incorrectly aligned with the query, while directly matching the query…
Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework
Ke Liu, Jiwei Wei, Shuchang Zhou +5
Supervised talking head forgery detection faces severe generalization challenges due to the continuous evolution of generators. By reducing reliance on generator-specific forgery p…
From Talking to Singing: A New Challenge for Audio-Visual Deepfake Detection
Ke Liu, Jiwei Wei, Wenyu Zhang +5
With rapid advances in audio-visual generative models, reliable forgery detection becomes increasingly critical. Existing methods for audio-visual deepfake detection typically rely…
RAVE: Re-Allocating Visual Attention in Large Multimodal Models
Xi Leng, Xinhong Ma, Ziqiang Dong +4
Large multimodal models (LMMs) inherit the self-attention mechanism of pretrained language backbones, yet standard attention can exhibit suboptimal allocation, including cross-moda…
HiMix: Hierarchical Artifact-aware Mixup for Generalized Synthetic Image Detection
Shuchang Zhou, Kaiwen Shen, Jiwei Wei +3
The rapid evolution of generative models has enabled the creation of highly realistic and diverse synthetic images, posing significant challenges to reliable and generalizable Synt…
Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection
Shuchang Zhou, Shangkun Wu, Jiwei Wei +4
AI-generated images are becoming increasingly realistic and diverse, posing significant challenges for generalizable detection. While Vision Foundation Models (VFMs) provide rich s…
CARD: Non-Uniform Quantization of Visual Semantic Unit for Generative Recommendation
Yibiao Wei, Jie Zou, Pengfei Zhang +4
Generative recommendation frameworks typically represent items as discrete Semantic IDs (SIDs). While existing studies have sought to enhance SID construction by incorporating mult…
ACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
Yang Yang, Feifan Meng, Han Fang +1
Diffusion models have achieved remarkable success in image generation, yet their training is predominantly driven by full-reference objectives that enforce pixel-wise similarity to…
Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization
Kaiyang Xing, Han Fang, Zhaoyun Chen +4
Recent studies show that quantum neural networks (QNNs) generalize well in few-shot regimes. To extend this advantage to large-scale tasks, we propose Q-LoRA, a quantum-enhanced fi…
TC-Padé: Trajectory-Consistent Padé Approximation for Diffusion Acceleration
Benlei Cui, Shaoxuan He, Bukun Huang +8
Despite achieving state-of-the-art generation quality, diffusion models are hindered by the substantial computational burden of their iterative sampling process. While feature cach…
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning
Yiming Shi, Jiwei Wei, Yujia Wu +4
The rapid growth of model scale has necessitated substantial computational resources for fine-tuning. Existing approach such as Low-Rank Adaptation (LoRA) has sought to address the…
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
Chengwei Sun, Jiwei Wei, Yujia Wu +5
Large pre-trained models (LPMs) have demonstrated exceptional performance in diverse natural language processing and computer vision tasks. However, fully fine-tuning these models…
DiffLoRA: Generating Personalized Low-Rank Adaptation Weights with Diffusion
Yujia Wu, Yiming Shi, Jiwei Wei +3
Personalized text-to-image generation has gained significant attention for its capability to generate high-fidelity portraits of specific identities conditioned on user-defined pro…