arXiv:2407.12002v1 Announce Type: new
Abstract: Recently, live streaming platforms have gained immense popularity. Traditional video highlight detection mainly focuses on visual features and utilizes both past and future content for prediction. However, live streaming requires models to infer without future frames and process complex multimodal interactions, including images, audio and text comments. To address these issues, we propose a multimodal transformer that incorporates historical look-back windows. We introduce a novel Modality Temporal Alignment Module to handle the temporal shift of cross-modal signals. Additionally, using existing datasets with limited manual annotations is insufficient for live streaming whose topics are constantly updated and changed. Therefore, we propose a novel Border-aware Pairwise Loss to learn from a large-scale dataset and utilize user implicit feedback as a weak supervision signal. Extensive experiments show our model outperforms various strong baselines on both real-world scenarios and public datasets. And we will release our dataset and code to better assess this topic.
Expert Commentary: The Rise of Multimodal Transformers in Live Streaming Platforms
Live streaming platforms have seen a tremendous surge in popularity in recent years, with millions of users streaming videos in real-time. With this surge, there has been a growing need for effective video highlight detection methods that can handle the complexities of live streaming, including multimodal interactions such as images, audio, and text comments.
Traditional video highlight detection models have primarily focused on visual features and utilized past and future content for prediction. However, live streaming presents unique challenges as models need to make inferences without access to future frames and also handle complex interactions across multiple modalities. To address these challenges, researchers have proposed a cutting-edge solution – multimodal transformers.
Multimodal transformers leverage the power of transformer-based architectures, which have proven to be highly effective in natural language processing tasks. By incorporating historical look-back windows, these models can make predictions based on past information and handle the temporal shift of cross-modal signals, ensuring accurate and robust detection of video highlights in live streaming scenarios.
What makes multimodal transformers particularly exciting is their multi-disciplinary nature. They combine techniques from computer vision, natural language processing, and machine learning to process and analyze a variety of input modalities. This cross-disciplinary approach allows for a richer understanding of the content and enables more sophisticated feature extraction and prediction capabilities.
Furthermore, the article highlights the challenge of obtaining annotated datasets for live streaming scenarios, where topics are constantly changing and updating. Traditional approaches that rely on limited manual annotations are not suitable in this dynamic context. To overcome this limitation, the authors propose a novel Border-aware Pairwise Loss, which leverages a large-scale dataset and utilizes user implicit feedback as a weak supervision signal. This innovative approach not only improves the training process but also provides a means to learn from the constantly shifting landscape of live streaming topics.
The application of multimodal transformers in live streaming platforms is highly relevant to the wider field of multimedia information systems. These systems aim to efficiently process, analyze, and retrieve multimedia content, and the integration of multimodal transformers provides a powerful tool for extracting meaningful information from live streaming datastreams. Moreover, given the cross-modal nature of live streaming platforms, the concepts of animations, artificial reality, augmented reality, and virtual realities are intricately linked to the field. The ability of multimodal transformers to effectively handle interactions between visual, audio, and textual modalities paves the way for more immersive and interactive experiences in these domains.
In conclusion, the proposed multimodal transformer framework represents an important advancement in the field of live streaming video highlight detection. Its ability to handle complex multimodal interactions and temporal shift of signals sets it apart from traditional approaches. The multi-disciplinary nature of the concepts involved, as well as their connection to the wider field of multimedia information systems and related domains, further underscores the significance of this research. The release of the dataset and code by the authors will undoubtedly contribute to the assessment and development of this rapidly evolving topic.