Cross Modal LMM for Video and Audio Analysis

With the rapid growth of video content, extracting meaningful insights has become crucial for effective retrieval and summarization. This paper introduces a Cross-Modal Large Multimodality Model (CMLMM) for video analysis, which integrates audio, text, and visual data to improve information retrieval. The system processes video frames, transcribes multilingual audio, and creates semantic representations for text and images, facilitating advanced applications like video summarization, question answering, and speaker identification. Current multimodal models encounter difficulties in temporal reasoning, structured retrieval, and multimodal integration, which restrict their capacity to extract key insights from prolonged videos. To overcome these challenges, CMLMM employs cutting-edge models for speech transcription, multimodal data processing, and video understanding. Experimental results indicate that Qwen2-VL-2B-Instruct surpasses LLaMA-3.2-11B Vision Instruct in essential tasks such as speaker identification and query-based video retrieval, resulting in more precise and efficient video analysis. This research enhances video comprehension, automated content summarization, and real-time information retrieval, with potential applications in education, media analysis, and interactive AI systems. The proposed end-to-end system allows for smooth video summarization, question answering, and object recognition through advanced speech transcription, semantic embedding, and retrieval-based response generation.