Selective Structured State-Spaces for Long-Form Video Understanding (2303.14526v1)

Abstract: Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all image-tokens equally as done by S4 model can adversely affect its efficiency and accuracy. To address this limitation, we present a novel Selective S4 (i.e., S5) model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos. Unlike previous mask-based token reduction methods used in transformers, our S5 model avoids the dense self-attention calculation by making use of the guidance of the momentum-updated S4 model. This enables our model to efficiently discard less informative tokens and adapt to various long-form video understanding tasks more effectively. However, as is the case for most token reduction methods, the informative image tokens could be dropped incorrectly. To improve the robustness and the temporal horizon of our model, we propose a novel long-short masked contrastive learning (LSMCL) approach that enables our model to predict longer temporal context using shorter input videos. We present extensive comparative results using three challenging long-form video understanding datasets (LVU, COIN and Breakfast), demonstrating that our approach consistently outperforms the previous state-of-the-art S4 model by up to 9.6% accuracy while reducing its memory footprint by 23%.

Overview of Selective Structured State-Spaces for Long-Form Video Understanding

The paper "Selective Structured State-Spaces for Long-Form Video Understanding" addresses the complex problem of spatiotemporal dependencies within long-form video sequences. Recognizing the limitations of current models such as the Structured State-Space Sequence (S4), the authors introduce a novel approach—the Selective S4 (S5) model. The primary innovation of the S5 model is its use of adaptive token selection through a lightweight mask generator that leverages a momentum-updated S4 model. This mechanism selectively identifies significant image tokens, optimizing both efficiency and accuracy in modeling long-term dependencies in video content.

Key Contributions

1. Adaptive Token Selection:
   • The S5 model incorporates a mask generator to selectively choose informative tokens, addressing shortcomings in the S4 model where all tokens are treated equally. This selective approach mitigates computation costs and enhances task-adaptability.
   • The momentum-updated S4 model feeds into the mask generator, leveraging S4 features without dense self-attention computations typical in transformers. This is an efficient alternative, particularly beneficial in terms of computational demands.
2. Long-Short Masked Contrastive Learning (LSMCL):
   • To counteract potential errors in token selection, the authors propose LSMCL. This pretraining strategy enables the model to predict longer temporal contexts from shorter video inputs, inherently increasing robustness.
   • Empirical results indicate significant accuracy improvements—up to 9.6% over previous methodologies—while reducing memory footprint by 23%.

Implementation and Results

The authors conducted comprehensive experiments on three datasets - LVU, COIN, and Breakfast - widely recognized for challenging benchmarks in video understanding. The novel S5 model consistently outperformed prior state-of-the-art models across various metrics.

1. LVU Dataset:
   • Tested across nine distinct tasks, the S5 model improved accuracy across content understanding, metadata prediction, and user engagement by substantial margins.
   • The S5 model demonstrated up to 9.6% improvement on these tasks, alongside reduced GPU memory usage by approximately 25%.
2. COIN and Breakfast Datasets:
   • Achieved significant gains in accuracy over previous models, highlighting the robustness and adaptability of the S5 model in handling complex procedural activities.

Implications and Future Work

The introduction of the S5 model has several implications for advancements in AI:

• Scalability and Efficiency: The model's ability to selectively process informative tokens promises scalability for video understanding tasks requiring long-term spatiotemporal reasoning.
• Robustness in Feature Selection: Combining token selection with LSMCL pretraining enhances the model's resilience against incorrect token discarding, ensuring reliability in various contexts.
• Potential for Broader Applications: The selective state-space approach opens pathways for improvements in other domains requiring judicious data processing, such as automated annotation, efficient compute resource allocation, and beyond.

Looking ahead, further refinement in adaptive token learning mechanisms can deepen insights into spatiotemporal dynamics, enabling AI systems to better interpret and generate narratives from video data. Implementations in related fields, such as natural language processing, could benefit from similar efficiency strategies in token manipulation.

In conclusion, the paper presents a substantial contribution to video processing methodologies, offering a robust, scalable, and efficient framework for long-form video understanding through the Selective S4 model.