Use What You Have: Video Retrieval Using Representations From Collaborative Experts (1907.13487v2)

Abstract: The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity, with some queries describing specific details such as the names of famous identities, content from speech, or text available on the screen. Our goal is to condense the multi-modal, extremely high dimensional information from videos into a single, compact video representation for the task of video retrieval using free-form text queries, where the degree of specificity is open-ended. For this we exploit existing knowledge in the form of pre-trained semantic embeddings which include 'general' features such as motion, appearance, and scene features from visual content. We also explore the use of more 'specific' cues from ASR and OCR which are intermittently available for videos and find that these signals remain challenging to use effectively for retrieval. We propose a collaborative experts model to aggregate information from these different pre-trained experts and assess our approach empirically on five retrieval benchmarks: MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet. Code and data can be found at www.robots.ox.ac.uk/~vgg/research/collaborative-experts/. This paper contains a correction to results reported in the previous version.

Overview of the Collaborative Experts Framework for Video Retrieval

The paper "Use What You Have: Video Retrieval Using Representations From Collaborative Experts" presents a novel approach to video retrieval using a framework referred to as Collaborative Experts. The authors aim to address the challenge of retrieving video content using natural language queries by leveraging pre-trained embeddings from multiple domain-specific experts. This method aggregates high-dimensional, multi-modal video information into a singular, compact representation, facilitating efficient and accurate video retrieval.

Key Contributions

The paper's primary contributions are articulated in three areas:

1. Collaborative Experts Framework: The introduction of a framework that combines a collection of pre-trained embeddings into a singular, joint video representation. This method allows for efficient offline computation and indexing, independent of text queries, thereby enhancing retrieval efficiency.
2. Utilization of General and Specific Features: The authors explore general video features like motion, audio, and image classification, as well as more specific features such as text and speech obtained via OCR and ASR. The findings highlight that while strong generic features provide good performance, specific features present challenges in their application for retrieval tasks.
3. Empirical Evaluation Across Benchmarks: The performance of the proposed method is assessed on multiple video retrieval benchmarks, including MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet, showing an advantage over prior approaches in several cases.

Methodology

The authors propose a collaborative experts model that leverages multiple pre-trained domain-specific embeddings. These include embeddings from objects, actions, scenes, faces, audio, and speech. The collaborative framework employs a dynamic attention mechanism that evaluates and filters representations from each expert, promoting collaboration between various video features.

The video encoder combines these embeddings and applies a gated embedding module to transform them into a joint video representation. The text-query encoder forms an independent textual representation using pretrained word embeddings and textual aggregation. This independent approach ensures efficient retrieval by pre-computing video embeddings offline.

Experimental Evaluation

The framework is evaluated across several benchmarks. Notable improvements in retrieval performance are reported, especially over prior state-of-the-art methods, such as MoEE. Through a detailed ablation paper, the paper validates the effectiveness of the collaborative approach and further explores the impact of different experts and the number of textual annotations used during training.

Implications and Future Work

The findings suggest significant potential for the collaborative experts framework within video retrieval contexts, particularly for handling heterogeneous video content efficiently. The approach underscores the importance of leveraging pre-existing, large-scale annotated datasets for training domain-specific experts.

Future directions could explore the generalizability of this framework to other video understanding tasks, such as clustering and summarization, expanding the utility and applicability of the method across diverse video analysis scenarios.

In summary, this paper contributes to the video retrieval domain by proposing a novel methodology that leverages collaborative embeddings from domain-specific experts. This method enhances retrieval efficiency and accuracy while highlighting challenges associated with the integration of specific video features in embedding spaces.