Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models (2412.18609v2)

Abstract: We present an efficient encoder-free approach for video-language understanding that achieves competitive performance while significantly reducing computational overhead. Current video-LLMs typically rely on heavyweight image encoders (300M-1.1B parameters) or video encoders (1B-1.4B parameters), creating a substantial computational burden when processing multi-frame videos. Our method introduces a novel Spatio-Temporal Alignment Block (STAB) that directly processes video inputs without requiring pre-trained encoders while using only 45M parameters for visual processing - at least a 6.5$\times$ reduction compared to traditional approaches. The STAB architecture combines Local Spatio-Temporal Encoding for fine-grained feature extraction, efficient spatial downsampling through learned attention and separate mechanisms for modeling frame-level and video-level relationships. Our model achieves comparable or superior performance to encoder-based approaches for open-ended video question answering on standard benchmarks. The fine-grained video question-answering evaluation demonstrates our model's effectiveness, outperforming the encoder-based approaches Video-ChatGPT and Video-LLaVA in key aspects like correctness and temporal understanding. Extensive ablation studies validate our architectural choices and demonstrate the effectiveness of our spatio-temporal modeling approach while achieving 3-4$\times$ faster processing speeds than previous methods. Code is available at https://jh-yi.github.io/Video-Panda.

• The paper introduces a parameter-efficient model that uses the Spatio-Temporal Alignment Block (STAB) to eliminate traditional encoders.
• It achieves competitive video question answering performance on benchmarks like MSVD-QA and TGIF-QA while processing 3-4x faster than encoder-based methods.
• The approach employs Learnable Selective Downsampling to reduce spatial redundancy, enhancing fine-grained spatio-temporal understanding.

Overview of Video-Panda: An Encoder-Free Video-LLM

The paper "Video-Panda: Parameter-efficient Alignment for Encoder-free Video-LLMs" presents a novel approach to video-language understanding that circumvents the typical reliance on computationally intensive image or video encoders. By introducing the Spatio-Temporal Alignment Block (STAB), the authors propose a method that directly processes video inputs without pre-trained encoders, achieving noteworthy performance using significantly fewer parameters—only 45 million for visual processing compared to the hundreds of millions required by current state-of-the-art models.

Key Contributions

The core contribution of this work is the development of an encoder-free model that eschews traditional vision encoders in favor of the STAB, a specialized mechanism designed to manage spatio-temporal contexts effectively. This model demonstrates:

• Parameter Efficiency: Video-Panda utilizes only 45M parameters, representing at least a 6.5x reduction compared to approaches like Video-ChatGPT and even more so compared to Video-LLaVA, leveraging design simplicity and efficiency.
• Superior Performance: The model achieves competitive or superior open-ended video question answering performance on benchmarks such as MSVD-QA and others, surpassing heavyweight models in several key metrics like correctness and temporal understanding.
• Faster Processing: With 3-4x faster processing speeds than encoder-based methods, the architecture improves the practical usability of video-LLMs across various applications.

Technical Advancements

This paper introduces the STAB framework, which operates by modeling spatio-temporal data through localized and global aggregations. The architecture includes:

• Local Spatio-Temporal Encoding (LSTE) for capturing fine-grained temporal and spatial patterns within video frames.
• Global Spatio-Temporal Relationship Aggregator (GSTRA) for video-level context aggregation across frames.
• Frame-wise Spatial Relationship Aggregator (FSRA) for capturing spatial context within each frame, allowing for a comprehensive understanding of frame-specific details.

An important methodological strategy is the removal of high-resolution spatial redundancy through a Learnable Selective Downsampling (LSD) technique, which efficiently reduces dimensions without significant information loss.

Experimental Evaluation

The empirical results substantiate the model's capabilities, with extensive evaluations performed on datasets such as MSVD-QA, MSRVTT-QA, TGIF-QA, and ActivityNet-QA. Video-Panda not only holds its own against models trained on the same dataset but also outperforms in fine-grained aspects of video understanding, such as correctness and temporal sequencing.

Ablation studies reveal that each component of the STAB plays a vital role in the model's performance. The comparative analysis against prior models underscores the value of effective parameter utilization, showcasing Video-Panda's unique architecture that preserves critical spatio-temporal information without a burdensome computational footprint.

Implications and Future Work

The implications of this work are significant for the field of video-language interfacing, particularly in applications demanding real-time or resource-constrained environments. The possibility of deploying efficient video-LLMs in settings where computational resources are limited opens up new avenues for AI integration across diverse platforms, including mobile and edge devices.

Future research could explore extending the flexibility of Video-Panda to handle complex multi-modal tasks, enhance its robustness across diverse datasets, and refine its adaptability in different linguistic and cultural contexts. Additionally, the integration of audio or other sensory data could further bolster the model's comprehension capabilities, creating more holistic and contextually aware systems.

In summary, this paper highlights a direction in video-LLM development that advocates for efficiency without compromising performance, providing a sustainable path forward in the progression of AI technologies.