Revealing Single Frame Bias for Video-and-Language Learning

Abstract: Training an effective video-and-LLM intuitively requires multiple frames as model inputs. However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more frames. In this work, we explore single-frame models for video-and-language learning. On a diverse set of video-and-language tasks (including text-to-video retrieval and video question answering), we show the surprising result that, with large-scale pre-training and a proper frame ensemble strategy at inference time, a single-frame trained model that does not consider temporal information can achieve better performance than existing methods that use multiple frames for training. This result reveals the existence of a strong "static appearance bias" in popular video-and-language datasets. Therefore, to allow for a more comprehensive evaluation of video-and-LLMs, we propose two new retrieval tasks based on existing fine-grained action recognition datasets that encourage temporal modeling. Our code is available at https://github.com/jayleicn/singularity

• The paper's main contribution shows that single-frame models, with strategic pre-training and ensemble inference, can rival multi-frame approaches in static-biased datasets.
• The study implements a vision-language encoder pairing and evaluates performance on tasks like text-to-video retrieval and video question answering.
• Empirical comparisons across datasets reveal that static appearance biases favor single-frame methods, prompting new benchmarks for temporal modeling.

A Study on Single Frame Bias in Video-and-Language Learning

The paper "Revealing Single Frame Bias for Video-and-Language Learning" by Jie Lei, Tamara L. Berg, and Mohit Bansal provides a detailed examination of the surprising efficacy of single-frame models in video-and-language tasks, challenging prevailing assumptions on the necessity of multiple frames for effective temporal modeling. This work systematically evaluates the performance of a single-frame approach and suggests that large-scale pre-training and robust inference strategies can compensate for the lack of temporality inherently present in single-frame models.

Key Findings and Methodology

The authors investigate whether the reliance on multiple frames in conventional video-and-LLMs justifies the increased computational and memory demands. Their findings suggest that, for many existing video-and-language datasets, single-frame models—with careful pre-training and inference methods—can outperform multi-frame methods. This performance is attributed to what the authors describe as "static appearance bias" in the datasets, where models learn to leverage static cues rather than temporal dynamics.

The study involves:

1. Implementing a single-frame approach using a vision encoder paired with a language encoder, subsequently integrated through a multi-modal encoder with cross-attention for fusion.
2. Conducting large-scale pre-training on both image-text and video-text datasets, allowing the single-frame model to learn robust visual and textual features before fine-tuning it on specific downstream tasks—text-to-video retrieval and video question answering.
3. Employing an early fusion ensemble strategy at inference time, which combines information from multiple frames to improve the accuracy and stability of predictions.

Empirical Results and Performance Implications

The empirical section presents a thorough comparison across several datasets, such as MSRVTT, DiDeMo, and ActivityNet Captions, where the single-frame model (dubbed "Singularity") consistently achieves competitive or superior results compared to existing multi-frame methods. Notably, it sets new performance benchmarks in some tasks, illustrating the potential of single-frame methods for certain types of data where static elements are overwhelmingly informative.

Despite these successes, the paper acknowledges the limitations of single-frame approaches on temporal-heavy tasks. To address this, the authors propose new benchmark tasks using the Something-Something v2 dataset that emphasize temporal modeling, aiming to better evaluate a model's capability to understand temporal sequences.

Broader Implications and Future Directions

The findings prompt a critical reflection on the biases present in current video-language datasets and their representation of temporal dynamics. The paper’s methodological innovations highlight the importance of pre-training scale and frame ensemble strategies, offering promising avenues for more computationally efficient video-LLMs.

For future developments, the authors suggest that further disentangling of visual appearance from temporal features within datasets could guide the creation of benchmarks that more accurately assess temporal understanding. Additionally, integrating improved temporal encoding methods while maintaining the efficiency of single-frame approaches could lead to more balanced models capable of handling a broader range of video-and-language tasks.

Conclusion

This paper significantly contributes to our understanding of video-and-language learning by revealing the potential and limitations of single-frame models. By exposing biases in prevalent datasets and proposing new benchmarks, this work sets the stage for developing more temporally-aware learning algorithms, paving the way for future research into efficient and effective video-and-language frameworks.