VideoLLM-online: Online Video Large Language Model for Streaming Video (2406.11816v1)

Abstract: Recent LLMs have been enhanced with vision capabilities, enabling them to comprehend images, videos, and interleaved vision-language content. However, the learning methods of these large multimodal models typically treat videos as predetermined clips, making them less effective and efficient at handling streaming video inputs. In this paper, we propose a novel Learning-In-Video-Stream (LIVE) framework, which enables temporally aligned, long-context, and real-time conversation within a continuous video stream. Our LIVE framework comprises comprehensive approaches to achieve video streaming dialogue, encompassing: (1) a training objective designed to perform LLMing for continuous streaming inputs, (2) a data generation scheme that converts offline temporal annotations into a streaming dialogue format, and (3) an optimized inference pipeline to speed up the model responses in real-world video streams. With our LIVE framework, we built VideoLLM-online model upon Llama-2/Llama-3 and demonstrate its significant advantages in processing streaming videos. For instance, on average, our model can support streaming dialogue in a 5-minute video clip at over 10 FPS on an A100 GPU. Moreover, it also showcases state-of-the-art performance on public offline video benchmarks, such as recognition, captioning, and forecasting. The code, model, data, and demo have been made available at https://showlab.github.io/videoLLM-online.

An Analytical Overview of VideoLLM-online: Online Video LLM for Streaming Video

The paper presents VideoLLM-online, a contemporary approach to integrating LLMs with video streaming capabilities, addressing the cinema-tography of temporal alignment, context management, and real-time interaction within continuous video streams. The authors elucidate a novel framework named Learning-In-Video-Stream (LIVE), integrated with the LLM architectures Llama-2 and Llama-3. This framework is designed to enhance the existing cloud of video comprehension by offering a real-time dialogue experience, marking a development in the field of AI assistants, particularly those embedded within augmented reality applications such as smart AR glasses.

Core Contributions and Methods

The paper introduces three principal advancements in the LIVE framework:

1. Enhanced Training Objective: The research presents a shifted paradigm in training strategies, coined as Streaming EOS Prediction. This method improves LLMs' ability to discern when to articulate responses and when to remain dormant, optimizing LLMing for contexts with streaming inputs. This is a deviation from the conventional practice of video chunk processing by segment, aiming to provide smooth interaction continuity sensitive to temporal demands.
2. Data Generation Scheme: The paper provides a synthetic dialogue generation scheme that translates offline temporal annotations into a dynamic streaming dialogue format via dataset conversions. This methodology fills the gap created by a dearth of available streaming video annotations, offering a scalable approach to developing language interchanges within video dialogues.
3. Optimized Inference Pipeline: The paper discusses an improved inference pipeline aimed at accelerating real-world video stream processing, enabling VideoLLM-online to support dialogues exceeding a 10 FPS threshold on an A100 GPU. This ensures that the inference mechanics align efficiently with the architecture's real-time service deliverables.

Evaluation and Results

The reported empirical comparisons show that VideoLLM-online stands out in supporting streaming dialogues across a 5-minute video while maintaining a fluid fps rate, surpassing the efficacy of prior models. The framework exhibits robust performance across various domains, including video benchmarking tasks such as scene recognition, summarization, and forecasting, thereby underlining its comprehensive applicability.

The model's ability to manage temporal alignment, real-time interaction, and maintain memory efficiency while generating responses was benchmarked against streaming narrations derived from the Ego4D dataset. The capabilities were measured using standard language perplexity metrics and new ones designed to assess temporal responsiveness and overall streaming fluency, clearly demonstrating the model's improvements over traditional methods.

Implications and Future Directions

The implications of this research extend across potential advancements in real-time AI assistant development. The realization of always-on, context-aware AI systems within industries has the potential to transform guidance services, user interfacing, and various real-world applications reliant on continuous video input. The researchers' foresight places emphasis on equipping BERT-based dialogue systems to adaptively respond to natural user queries based on streaming visual inputs.

Future developments should investigate the scaling of this method across larger datasets and fostering more robust embeddings capable of capturing spatial interactions alongside temporal continuity. Expansion of the dialogue capability could be considered by enriching datasets used for training with diverse real-world interaction patterns. Moreover, simultaneous developments in processor efficiency and architecture design may provide enhancements that bring VideoLLM-online closer to a fully viable solution for deployment in commercial AR settings.

Through this investigation, VideoLLM-online sets a critical juncture as a blueprint for integrating LLMs within real-time video streams, specifically tailored towards augmenting human-computer interaction efficiency in extensive and dynamic visual environments.