Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling (2102.06183v1)

Abstract: The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision models and text features from LLMs. These feature extractors are trained independently and usually on tasks different from the target domains, rendering these fixed features sub-optimal for downstream tasks. Moreover, due to the high computational overload of dense video features, it is often difficult (or infeasible) to plug feature extractors directly into existing approaches for easy finetuning. To provide a remedy to this dilemma, we propose a generic framework ClipBERT that enables affordable end-to-end learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that ClipBERT outperforms (or is on par with) existing methods that exploit full-length videos, suggesting that end-to-end learning with just a few sparsely sampled clips is often more accurate than using densely extracted offline features from full-length videos, proving the proverbial less-is-more principle. Videos in the datasets are from considerably different domains and lengths, ranging from 3-second generic domain GIF videos to 180-second YouTube human activity videos, showing the generalization ability of our approach. Comprehensive ablation studies and thorough analyses are provided to dissect what factors lead to this success. Our code is publicly available at https://github.com/jayleicn/ClipBERT

Overview of ClipBERT: Sparse Sampling for Video-and-Language Learning

The paper introduces ClipBERT, a framework designed to enhance video-and-language learning by leveraging sparse sampling techniques. This approach deviates from the traditional methods that rely on densely extracted features from full-length videos, thereby reducing computational overhead and enabling end-to-end model training. The central idea is that less is indeed more; by using only a few sparsely sampled clips during training, ClipBERT demonstrates superior performance in comparison to conventional methods that utilize dense features.

Key Contributions

1. Sparse Sampling Strategy: ClipBERT uses only a few short clips from a video at each training step, reducing memory usage and computational demands. This approach allows end-to-end learning directly from raw video frames and text tokens, facilitating more efficient video-and-language task learning.
2. Image-text Pre-training: The framework reutilizes image-text pre-training, traditionally used for image-based tasks, to improve video-text understanding. This cross-modal pre-training bridges the gap between visual and textual modalities, enhancing ClipBERT's performance in video-and-language tasks.
3. End-to-End Learning: The framework ensures that models are trainable in an end-to-end manner, allowing task-specific finetuning that optimizes feature representations, leading to improved performance over traditional methods that use offline extracted features.

Experimental Results

Extensive experiments were conducted across two primary video-and-language tasks: text-to-video retrieval and video question answering. ClipBERT was evaluated on multiple datasets, including MSRVTT, DiDeMo, ActivityNet Captions, and TGIF-QA. The results reveal that ClipBERT consistently outperforms state-of-the-art methods in these domains, even those utilizing comprehensive pre-trained features from large datasets like HowTo100M.

Theoretical and Practical Implications

The sparse sampling approach underscores the potential benefits of using minimal data to achieve maximum learning efficiency in AI models. The success of sparse sampling suggests that key semantic information can be captured without relying on exhaustive feature extraction. This aligns with practical needs for reducing computational resources while maintaining or enhancing performance.

Future Directions

The research opens avenues for further exploration in sparse sampling techniques, potentially leading to advancements in other multimodal learning tasks. One potential direction might explore integrating additional modalities, such as audio inputs, to further enrich the model's contextual understanding. The framework could also adapt to newer datasets with higher resolutions, potentially improving performance as computational efficiency is achieved with more powerful hardware.

By focusing on the "less is more" principle, ClipBERT has established a promising avenue for future research and application in video-and-language understanding tasks, highlighting the significance of efficient sampling strategies in multimodal AI systems.