Vision-Language Models pre-trained on large-scale image-text datasets have
shown superior performance in downstream tasks such as image retrieval. Most of
the images for pre-training are presented in the form of open domain
common-sense visual elements. Differently, video covers in short video search
scenarios are presented as user-originated contents that provide important
visual summaries of videos. In addition, a portion of the video covers come
with manually designed cover texts that provide semantic complements. In order
to fill in the gaps in short video cover data, we establish the first
large-scale cover-text benchmark for Chinese short video search scenarios.
Specifically, we release two large-scale datasets CBVS-5M/10M to provide short
video covers, and the manual fine-labeling dataset CBVS-20K to provide real
user queries, which serves as an image-text benchmark test in the Chinese short
video search field. To integrate the semantics of cover text in the case of
modality missing, we propose UniCLIP where cover texts play a guiding role
during training, however are not relied upon by inference. Extensive evaluation
on CBVS-20K demonstrates the excellent performance of our proposal. UniCLIP has
been deployed to Tencent’s online video search systems with hundreds of
millions of visits and achieved significant gains. The complete dataset, code
and checkpoints will be available upon release.
As an expert commentator, I find this article fascinating as it explores the intersection of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The concept of utilizing vision-language models pre-trained on large-scale image-text datasets is not only innovative but also shows promise for improving various applications in the field.
This article specifically focuses on the use of these models in short video search scenarios. Unlike image retrieval tasks, where pre-training images are usually open domain common-sense visual elements, video covers in short video search scenarios are user-originated contents that provide important visual summaries of videos. This distinction poses a challenge in training models that can effectively understand and extract relevant information from the video covers.
To address this challenge, the authors introduce the first large-scale cover-text benchmark for Chinese short video search scenarios. They provide two large-scale datasets, CBVS-5M/10M, which offer short video covers, and a manual fine-labeling dataset, CBVS-20K, which provides real user queries. These datasets serve as valuable resources for training and evaluating vision-language models in the Chinese short video search field.
One notable aspect of this research is the integration of cover text semantics. In cases where modality is missing, the authors propose a novel approach called UniCLIP. UniCLIP leverages cover texts during training to guide the model’s learning process but does not rely on them during inference. This method ensures that the model can understand and utilize cover text information when available but can still perform well in cases where it is absent.
The authors conducted extensive evaluations on the CBVS-20K dataset and demonstrated the exceptional performance of their UniCLIP proposal. Furthermore, they have deployed UniCLIP to Tencent’s online video search systems, which receive hundreds of millions of visits. The significant gains achieved with UniCLIP highlight its efficacy and potential value for real-world applications.
In conclusion, this research contributes to the wider field of multimedia information systems by addressing the unique challenges in short video search scenarios. By introducing large-scale datasets and proposing a novel approach that integrates cover text semantics, the authors have made important advancements in the field. This work has implications for various areas such as animations, artificial reality, augmented reality, and virtual realities, as it provides a foundation for improving video search capabilities and enhancing user experiences in these domains.
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