Have you ever wondered how to interact with videos using natural language? How to ask questions, give commands, or generate captions for videos? If you are interested in these topics, you might want to check out Valley, a video assistant with large language model enhanced ability.
Valley is a novel framework that combines video understanding and language generation to enable natural and multimodal communication with videos. It was developed by a team of researchers from the University of Science and Technology of China, the Chinese Academy of Sciences. The project was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China, ByteDance Inc, Fudan University, Chongqing University, Beijing University of Posts and Telecommunications.
The motto behind the development of Valley was to create a video assistant that can understand and respond to natural language queries and commands in various scenarios, such as video search, video summarization, video captioning, video question answering, and video editing. The researchers wanted to leverage the power of large pre-trained language models to enhance the video understanding and language generation capabilities of Valley.
What is Valley?
Valley represents a comprehensive framework comprising three fundamental components: a video encoder, a language encoder, and a language decoder. To extract visual attributes from videos, the video encoder utilizes a convolutional neural network (CNN) in conjunction with a transformer network. On the other hand, the language encoder employs a large-scale, pre-trained language model like GPT-3 to encode natural language inputs, including queries and commands. The language decoder, powered by another pre-trained language model, generates meaningful natural language outputs, such as answers and captions.
At the heart of Valley lies a groundbreaking concept that employs these expansive pre-trained language models as knowledge sources for both video comprehension and language generation. The researchers have introduced an innovative technique known as video-language alignment, enabling the alignment of visual features and linguistic attributes within a shared semantic space. This novel approach empowers Valley to effectively utilize the extensive knowledge and linguistic capabilities of the pre-trained language models, enabling it to comprehend and respond to natural language inputs associated with videos.
Key Features of Valley
Some of the key features of Valley are:
- It can handle various types of natural language inputs, such as questions, commands, keywords, or sentences.
- It can generate various types of natural language outputs, such as answers, captions, summaries, or edits.
- It can perform multiple tasks related to video understanding and language generation, such as video search, video summarization, video captioning, video question answering, and video editing.
- It can adapt to different domains and scenarios by fine-tuning the pre-trained language models on specific datasets.
- It can achieve state-of-the-art results on several benchmarks for video understanding and language generation tasks.
- It can support multiple languages by using multi-lingual pre-trained language models.
- It can generate diverse and creative responses by using sampling strategies or beam search with diversity penalties.
Capabilities/Use Cases of Valley
Valley has many potential capabilities and use cases for interacting with videos using natural language. Here are some examples:
- Video search: You can use Valley to search for videos that match your natural language query. For example, you can ask “show me videos of cute cats playing with yarn” or “find me videos of people dancing salsa” and Valley will return relevant videos from its database.
- Video summarization: You can use Valley to generate a concise summary of a video using natural language. For example, you can ask “summarize this video in one sentence” or “give me three bullet points about this video” and Valley will produce a short summary that captures the main content and highlights of the video.
- Video captioning: You can use Valley to generate descriptive captions for videos using natural language. For example, you can ask “caption this video” or “describe what is happening in this video” and Valley will generate captions that describe the scenes, actions, objects, and events in the video.
- Video question answering: You can use Valley to answer questions about videos using natural language. For example, you can ask “who is the main character in this video?” or “what is the name of the song playing in this video?” and Valley will answer your questions based on the information in the video.
- Video editing: You can use Valley to edit videos using natural language commands. For example, you can ask “cut this video from 0:10 to 0:20” or “add subtitles to this video” and Valley will perform the editing operations according to your commands.
Architecture of Valley
To make the pre-trained LLM understand videos and adapt to different lengths of videos and images, researchers add a module that combines the features of each frame in the video encoder. They use the same structure as LLaVA, which connects the video features to the LLM with a simple layer. They choose Stable-Vicuna as the language interface because it has better multilingual chat skills. The overall architecture is shown in Figure below.
source - https://arxiv.org/pdf/2306.07207.pdf
Researchers take a video V and sample T frames at 1 FPS. Each image gets visual features from the pre-trained CLIP visual encoder (ViT-L/14). Each feature has 256 patches and 1 global feature (the “[CLS]” token). They use the average pooling method to combine the patch features of T frames in the time dimension. This gives one feature for each patch and one feature for the whole video.
How to access and use Valley?
Valley is an open-source project that can be accessed and used by anyone who is interested in interacting with videos using natural language. The source code, pre-trained models, datasets, and instructions are available on the GitHub repository. The researchers also provide a demo link where you can try out Valley online by uploading your own videos or choosing from some sample videos and entering natural language inputs. You can also see some examples of Valley’s outputs on the project website.
Valley is licensed under the Apache License 2.0, which means that you can use it for both personal and commercial purposes, as long as you follow the terms and conditions of the license. However, you should also be aware that Valley uses some third-party libraries and models that may have different licenses and restrictions. For example, GPT-3 is a proprietary model owned by OpenAI that requires a paid subscription to access its API. Therefore, you should check the licenses and permissions of the components that you use before deploying Valley in your own applications.
If you are interested to learn more about Valley, all relevant links are provided under the 'source' section at the end of this article.
Limitations
Valley is a novel and impressive framework that enables natural and multimodal communication with videos, but it also has some limitations that need to be addressed in future work. Some of the limitations are:
- Valley relies heavily on large pre-trained language models, which are expensive to train and run, and may not be accessible to everyone.
- Valley does not have a mechanism to handle noisy or ambiguous inputs, such as incomplete sentences, spelling errors, or vague queries.
- Valley does not have a mechanism to handle multimodal inputs or outputs, such as speech or gestures.
- Valley does not have a mechanism to handle feedback or dialogue with users, such as clarification questions, confirmation requests, or corrections.
Future Plans
Valley represents a promising framework that serves as a catalyst for further exploration and innovation within the realms of video comprehension and language generation. Nonetheless, there remain numerous challenges and untapped opportunities in this field. Here are some of the potential directions for future work:
- Advancing the development of efficient and scalable techniques for training and implementing large pre-trained language models, specifically tailored for video comprehension and language generation.
- Pioneering robust and flexible approaches to handle diverse and intricate natural language inputs and outputs, enabling seamless interaction with videos.
- Creating interactive and adaptive methodologies to process multimodal inputs and outputs, facilitating effective communication with videos.
- Cultivating collaborative and conversational techniques to engage in feedback and dialogue with users, enhancing the overall interaction with videos.
- Designing comprehensive and versatile methods to handle various types of videos, including live streams, 360-degree videos, and VR/AR videos.
- Crafting ethical and responsible practices to ensure the quality, fairness, privacy, and security of video comprehension and language generation.
Conclusion
Valley is a new framework that opens up new possibilities for interacting with videos using natural language. It combines video understanding and language generation to create a video assistant that can understand and respond to natural language queries and commands in various scenarios. It leverages the power of large pre-trained language models to enhance its video understanding and language generation capabilities. It achieves state-of-the-art results on several benchmarks for video understanding and language generation tasks.
Valley is not perfect, and it still has some challenges and limitations that need to be overcome in future work. Nevertheless, we believe that Valley is a promising framework that can inspire more research and innovation in the field of video understanding and language generation.
source
research paper - https://arxiv.org/abs/2306.07207
Git Hub repo - https://github.com/RupertLuo/Valley
valley project - https://valley-vl.github.io/
Demo link - https://ce9b4fd9f666cfca01.gradio.live/
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