Our contribution introduces a groundbreaking multimodal large language model designed to comprehend multi-images, multi-audio, and multi-images-multi-audio within a single multiturn session. Leveraging state-of-the-art models, we utilize the SigLIP encoder for visual inputs and the Whisper Encoder for audio inputs. Notably, this multimodal large language model is bilingual, proficient in understanding both English and Malay simultaneously. We proudly unveil two versions of this model: TinyLlama with 1.1B parameters, and Mistral with 7B parameters. With its ability to navigate diverse modalities and languages, our model represents a significant advancement for the Malaysian context and beyond.
All models released at https://huggingface.co/collections/mesolitica/multimodal-malaysian-llm-65c6f893e03f78fa9e5c8859
Bridging Ears and Eyes: Analyzing Audio and Visual Large Language Models to Humans in Visible Sound Recognition and Reducing Their Sensory Gap via Cross-Modal Distillation
Columbia University（哥伦比亚大学）；University of Washington（华盛顿大学）
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos
Southern University of Science and Technology（南方科技大学）；University of Birmingham（伯明翰大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Hong Kong（香港大学）；University of Manchester（曼彻斯特大学）
Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics
University of Copenhagen（哥本哈根大学）
本文旨在测试多模态方法的音乐情感识别（MER）是否比单模态的高层次的歌曲特征和歌词。我们使用从Spotify API检索的11个歌曲特征，组合歌词特征，包括情感，TF-IDF和Anew，以预测Deezer情绪检测数据集（DMDD）（Delbouys et al.，2018），具有4种不同的回归模型。我们发现，在11个高级歌曲特征中，主要有5个对性能有贡献，多模态特征在预测效价时比单独的音频做得更好。我们使我们的代码可供使用。
Pay More Attention To Audio: Mitigating Imbalance of Cross-Modal Attention in Large Audio Language Models
1Laboratory of Cognitive Computing；Application, College of Intelligence；Computing, Tianjin University, Tianjin, China 2School of Computer Science, Shanghai Jiao Tong University, Shanghai, China 3School of Electrical \& Electronic Engineering, Nanyang Technological University, Singapore 4Huiyan Technology (Tianjin) Co., Ltd, Tianjin, China
COMET: Concept Space Dissection of the Modality Gap in Audio-Text Multimodal Contrastive Embeddings
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Centre for Vision, Speech, and Signal Processing (CVSSP), University of Surrey（Surrey 大学视觉、语音和信号处理中心）
