Given a text query, partially relevant video retrieval (PRVR) seeks to find
untrimmed videos containing pertinent moments in a database. For PRVR, clip
modeling is essential to capture the partial relationship between texts and
videos. Current PRVR methods adopt scanning-based clip construction to achieve
explicit clip modeling, which is information-redundant and requires a large
storage overhead. To solve the efficiency problem of PRVR methods, this paper
proposes GMMFormer, a Gaussian-Mixture-Model based Transformer which models
clip representations implicitly. During frame interactions, we incorporate
Gaussian-Mixture-Model constraints to focus each frame on its adjacent frames
instead of the whole video. Then generated representations will contain
multi-scale clip information, achieving implicit clip modeling. In addition,
PRVR methods ignore semantic differences between text queries relevant to the
same video, leading to a sparse embedding space. We propose a query diverse
loss to distinguish these text queries, making the embedding space more
intensive and contain more semantic information. Extensive experiments on three
large-scale video datasets (i.e., TVR, ActivityNet Captions, and Charades-STA)
demonstrate the superiority and efficiency of GMMFormer. Code is available at
url{https://github.com/huangmozhi9527/GMMFormer}.
Expert Commentary: The Multi-Disciplinary Nature of Partially Relevant Video Retrieval (PRVR)
Partially Relevant Video Retrieval (PRVR) is a complex task that combines concepts from various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. This multi-disciplinary nature arises from the need to capture and understand the relationship between textual queries and untrimmed videos. In this expert commentary, we dive deeper into the concepts and discuss how PRVR methods like GMMFormer address challenges in the field.
The Importance of Clip Modeling in PRVR
In PRVR, clip modeling plays a crucial role in capturing the partial relationship between texts and videos. By constructing meaningful clips from untrimmed videos, the retrieval system can focus on specific moments that are pertinent to the query. Traditional PRVR methods often adopt scanning-based clip construction, which explicitly models the relationship. However, this approach suffers from information redundancy and requires a large storage overhead.
GMMFormer, a novel approach proposed in this paper, tackles the efficiency problem of PRVR methods by leveraging the power of Gaussian-Mixture-Model (GMM) based Transformers. Instead of explicitly constructing clips, GMMFormer models clip representations implicitly. By incorporating GMM constraints during frame interactions, the model focuses on adjacent frames rather than the entire video. This approach allows for multi-scale clip information to be encoded in the generated representations, achieving efficient and implicit clip modeling.
Tackling Semantic Differences in Text Queries
Another challenge in PRVR methods is handling semantic differences between text queries that are relevant to the same video. Existing methods often overlook these semantic differences, resulting in a sparse embedding space. To address this, the paper proposes a query diverse loss that distinguishes between text queries, making the embedding space more intensive and containing more semantic information.
Experiments and Results
The proposed GMMFormer approach is evaluated through extensive experiments on three large-scale video datasets: TVR, ActivityNet Captions, and Charades-STA. The results demonstrate the superiority and efficiency of GMMFormer in comparison to existing PRVR methods. The inclusion of multi-scale clip modeling and query diverse loss significantly enhances the retrieval performance and addresses the efficiency challenges faced by traditional methods.
Conclusion
Partially Relevant Video Retrieval (PRVR) is a fascinating field that involves concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The GMMFormer approach proposed in this paper showcases the multi-disciplinary nature of PRVR and its impact on clip modeling, semantic differences in text queries, and retrieval efficiency. Future research in this domain will likely explore more advanced techniques for implicit clip modeling and further focus on enhancing the embedding space to better capture semantic information.