The application aims to enable efficient human-AI conversations to extract crucial details from lengthy online videos. Currently, online videos often contain extraneous content, making it cognitively demanding for viewers to grasp key information. However, people prefer consuming concise, information-dense video summaries. The application facilitates natural dialogues between users and AI to provide on-demand video insights. The system analyzes input videos using computer vision and speech processing to build a rich textual context. This context is fed into a large language model that can generate abstractive summaries and meaningfully answer users' follow up questions.
The technical implementation involves first extracting key video frames and generating captions for them using state-of-the-art Language-Image Pre-training vision transformer models. In parallel, the audio is transcribed into text using the Log-Mel Transformer model. The frame captions and audio transcripts establish the contextual database for each video. When queried, this context prompts the language model to converse about the video content.
The application conversational interface allows users to request summaries, pose queries, and review dialogue history. This interaction model diverges from passive video watching and enables extracting crucial details without repetitive rewatching. The system is web-based for easy access. The application solves the problem of lengthy, hard-to-comprehend videos that overload users' limited time and attention. By distilling videos into concise summaries and allowing fluid human-AI dialogues, application enhances the video knowledge consumption experience. Users can rapidly grasp key information they need without wasting time.
The novelty lies in the fusion of video/audio analysis with interactive language models. Unlike prior work focused solely on video summarization, The application enables back-and-forth conversations to extract insights tailored to users' interests. The system requires no training data; the language model is prompt-tuned based on each video's generated context. This on-the-fly adaptation allows Intelli-Watch to handle diverse videos without predefined datasets.
In summary, application pioneers a human-centric approach for accessing impactful video knowledge. By extracting crucial content from lengthy videos and powering natural conversations with AI, The application aims to overcome the comprehension challenges of online video platforms. The proposed system facilitates more efficient and engaging video knowledge discovery.
Use Cases: efficiently watch length infotainment videos, can be included in video streaming applications such as netflix, prime, hulu etc, to query and interact with movies, and media content. This application can also be used to effectively trigger and extract anomalies in hours-long surveillance footage without actually watching them.