Enhancing Text-to-Video Retrieval by Leveraging Automatic Image Captioning

Core Concepts

Automatic image captioning models can provide a useful supervision signal to train text-to-video retrieval models on unlabeled video data, outperforming strong zero-shot baselines.

Abstract

The paper proposes a framework to train text-to-video retrieval models using automatic image captions as a source of supervision, without requiring any manually annotated video data.
Key highlights:

The authors leverage recent advances in image captioning models, such as ClipCap and BLIP, to automatically generate captions for video frames and use them as pseudo-labels for training.
They employ a caption selection strategy based on cross-modal similarity (CLIPScore) to filter out low-quality captions and retain the most relevant ones.
The authors extend the query-scoring temporal pooling method to incorporate multiple captions per video, which helps to capture the global video content beyond a single frame.
Experiments on three standard text-to-video retrieval benchmarks (ActivityNet, MSR-VTT, MSVD) show that the proposed approach outperforms strong zero-shot baselines like CLIP.
The authors also explore the impact of different image captioning models, caption selection strategies, and the benefits of combining multiple datasets during training.
Qualitative results demonstrate that the retrieved videos are semantically relevant to the text queries, even when the top-ranked video is not the exact ground-truth match.

Stats

"The length of the videos varies from 10s to 32s, with an average of 15s." (MSR-VTT dataset)
"Videos are segmented into 42k clips with an average length of 45s." (ActivityNet dataset)
"The dataset contains both short videos (∼1s) and long videos (∼60s)." (MSVD dataset)

Quotes

"We show that automatically labeling video frames with image captioning allows text-to-video retrieval training."
"We employ a filtering approach where we select the captions that better describe the frame by computing the CLIPScore metric [25]."
"We introduce multi-caption training to effectively use multiple textual labels per video, by extending the query-scoring method of [5]."

Key Insights Distilled From

Learning text-to-video retrieval from image captioning

by Luca... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17498.pdf

Deeper Inquiries

How can the proposed framework be extended to incorporate additional modalities beyond image captions, such as audio or video-level features, to further improve text-to-video retrieval performance

The proposed framework can be extended to incorporate additional modalities beyond image captions by leveraging multi-modal fusion techniques. One approach could be to integrate audio features extracted from the videos using methods like spectrogram analysis or audio embeddings. These audio features can be combined with the visual features from the image captioning models using fusion methods such as late fusion, early fusion, or attention mechanisms. By incorporating audio information, the model can capture more comprehensive representations of the videos, leading to improved text-to-video retrieval performance.
Another modality that can be integrated is video-level features. Instead of relying solely on frame-level information, aggregating features from multiple frames to create a holistic representation of the video can enhance the model's understanding of the video content. This can be achieved through techniques like temporal pooling, recurrent neural networks, or 3D convolutional neural networks. By incorporating video-level features along with image captions and audio features, the model can have a more robust and multi-faceted understanding of the videos, ultimately improving retrieval performance.

What are the potential limitations of using image captioning models as the sole source of supervision, and how could the approach be combined with self-supervised learning techniques on unlabeled video data

Using image captioning models as the sole source of supervision may have limitations in capturing the dynamic and temporal aspects of videos. Image captioning models are designed to describe static images and may not fully capture the context and progression of events in videos. To address this limitation, the approach can be combined with self-supervised learning techniques on unlabeled video data. Self-supervised learning methods, such as contrastive learning or temporal pretext tasks, can help the model learn meaningful representations of video content without the need for manual annotations.
By incorporating self-supervised learning, the model can leverage the inherent structure and information present in the unlabeled video data to improve its understanding of temporal relationships and dynamics. This combined approach can enhance the model's ability to retrieve relevant videos based on text queries by capturing both the visual content described in image captions and the temporal context learned through self-supervised learning. Additionally, self-supervised learning can help mitigate the limitations of relying solely on image captioning models, leading to more robust and comprehensive video retrieval performance.

Given the promising results on smaller datasets like MSVD, how could the framework be scaled to handle larger and more diverse video collections in a computationally efficient manner

To scale the framework to handle larger and more diverse video collections in a computationally efficient manner, several strategies can be employed. One approach is to leverage distributed computing resources, such as GPU clusters or cloud computing platforms, to parallelize the training process and accelerate model training on large datasets. By distributing the workload across multiple GPUs or nodes, the model can process a larger volume of data efficiently.
Additionally, techniques like data parallelism and model parallelism can be utilized to optimize the training process and handle larger datasets. Data parallelism involves splitting the dataset across multiple devices and updating the model parameters in parallel, while model parallelism involves splitting the model architecture across devices to handle larger models. By implementing these parallelization strategies, the framework can efficiently scale to process larger and more diverse video collections.
Furthermore, optimizing the model architecture and training pipeline for efficiency, such as using batch normalization, mixed precision training, and efficient data loading techniques, can help reduce training time and resource requirements. By fine-tuning the training process and architecture design for scalability, the framework can effectively handle the challenges posed by larger video datasets while maintaining computational efficiency.