2 citations · 2 across the 5 of their papers we have counts for
5 papers
Eureka-Audio: Triggering Audio Intelligence in Compact Language Models
Dan Zhang, Yishu Lei, Jing Hu +10
We present Eureka-Audio, a compact yet high-performance audio language model that achieves competitive performance against models that are 4 to 18 times larger across a broad range…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
CORD: Bridging the Audio-Text Reasoning Gap via Weighted On-policy Cross-modal Distillation
Jing Hu, Danxiang Zhu, Xianlong Luo +9
Large Audio Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large language models (LLMs), LALMs frequently exhibit a degrad…
MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free
Yishu Lei, Shuwei He, Jing Hu +9
Extending the input modality of Large Language Models~(LLMs) to the audio domain is essential for achieving comprehensive multimodal perception. However, it is well-known that acou…
Self-Enhanced Image Clustering with Cross-Modal Semantic Consistency
Zihan Li, Wei Sun, Jing Hu +3
While large language-image pre-trained models like CLIP offer powerful generic features for image clustering, existing methods typically freeze the encoder. This creates a fundamen…