The proliferation of short video and live-streaming platforms has
revolutionized how consumers engage in online shopping. Instead of browsing
product pages, consumers are now turning to rich-content e-commerce, where they
can purchase products through dynamic and interactive media like short videos
and live streams. This emerging form of online shopping has introduced
technical challenges, as products may be presented differently across various
media domains. Therefore, a unified product representation is essential for
achieving cross-domain product recognition to ensure an optimal user search
experience and effective product recommendations. Despite the urgent industrial
need for a unified cross-domain product representation, previous studies have
predominantly focused only on product pages without taking into account short
videos and live streams. To fill the gap in the rich-content e-commerce area,
in this paper, we introduce a large-scale cRoss-dOmain Product Ecognition
dataset, called ROPE. ROPE covers a wide range of product categories and
contains over 180,000 products, corresponding to millions of short videos and
live streams. It is the first dataset to cover product pages, short videos, and
live streams simultaneously, providing the basis for establishing a unified
product representation across different media domains. Furthermore, we propose
a Cross-dOmain Product rEpresentation framework, namely COPE, which unifies
product representations in different domains through multimodal learning
including text and vision. Extensive experiments on downstream tasks
demonstrate the effectiveness of COPE in learning a joint feature space for all
product domains.

通过引入名为 ROPE 的大规模 cRoss-dOmain Product Ecognition 数据集和 COPE 跨域产品表征框架，本文填补了富内容电子商务领域的空白，实现了不同媒体领域的统一产品表示和跨域产品识别，展示了 COPE 在学习所有产品领域的联合特征空间方面的有效性。

跨领域富内容电商产品表征学习

Cross-Domain Product Representation Learning for Rich-Content E-Commerce

With the widespread popularity of user-generated short videos, it becomes
increasingly challenging for content creators to promote their content to
potential viewers. Automatically generating appealing titles and covers for
short videos can help grab viewers' attention. Existing studies on video
captioning mostly focus on generating factual descriptions of actions, which do
not conform to video titles intended for catching viewer attention.
Furthermore, research for cover selection based on multimodal information is
sparse. These problems motivate the need for tailored methods to specifically
support the joint task of short video title generation and cover selection
(TG-CS) as well as the demand for creating corresponding datasets to support
the studies. In this paper, we first collect and present a real-world dataset
named Short Video Title Generation (SVTG) that contains videos with appealing
titles and covers. We then propose a Title generation and Cover selection with
attention Refinement (TCR) method for TG-CS. The refinement procedure
progressively selects high-quality samples and highly relevant frames and text
tokens within each sample to refine model training. Extensive experiments show
that our TCR method is superior to various existing video captioning methods in
generating titles and is able to select better covers for noisy real-world
short videos.

本文介绍了一种名为 Short Video Title Generation (SVTG) 的真实数据集，以及一种应用多模态信息的 Title generation and Cover selection with attention Refinement (TCR) 方法，通过对高质量样本和高相关性帧和文本令牌进行精细化训练，该方法在生成标题和选择封面方面优于现有的各种视频字幕方法，并且可以针对真实短视频选择更好的封面。

TCR: 基于注意力细化的短视频标题生成及封面选择

TCR: Short Video Title Generation and Cover Selection with Attention  Refinement

With short video platforms becoming one of the important channels for news
sharing, major short video platforms in China have gradually become new
breeding grounds for fake news. However, it is not easy to distinguish short
video rumors due to the great amount of information and features contained in
short videos, as well as the serious homogenization and similarity of features
among videos. In order to mitigate the spread of short video rumors, our group
decides to detect short video rumors by constructing multimodal feature fusion
and introducing external knowledge after considering the advantages and
disadvantages of each algorithm. The ideas of detection are as follows: (1)
dataset creation: to build a short video dataset with multiple features; (2)
multimodal rumor detection model: firstly, we use TSN (Temporal Segment
Networks) video coding model to extract video features; then, we use OCR
(Optical Character Recognition) and ASR (Automatic Character Recognition) to
extract video features. Recognition) and ASR (Automatic Speech Recognition)
fusion to extract text, and then use the BERT model to fuse text features with
video features (3) Finally, use contrast learning to achieve distinction: first
crawl external knowledge, then use the vector database to achieve the
introduction of external knowledge and the final structure of the
classification output. Our research process is always oriented to practical
needs, and the related knowledge results will play an important role in many
practical scenarios such as short video rumor identification and social opinion
control.

该研究探讨了利用多模式特征融合和引入外部知识来检测短视频假消息的方法，包括数据集构建，多模式谣言检测模型和对比学习技术，并在实际应用场景中发挥作用。

基于对比学习的多模式短视频谣言检测系统

Multimodal Short Video Rumor Detection System Based on Contrastive  Learning

Nowadays, live-stream and short video shopping in E-commerce have grown
exponentially. However, the sellers are required to manually match images of
the selling products to the timestamp of exhibition in the untrimmed video,
resulting in a complicated process. To solve the problem, we present an
innovative demonstration of multi-modal retrieval system called "Fashion
Focus", which enables to exactly localize the product images in the online
video as the focuses. Different modality contributes to the community
localization, including visual content, linguistic features and interaction
context are jointly investigated via presented multi-modal learning. Our system
employs two procedures for analysis, including video content structuring and
multi-modal retrieval, to automatically achieve accurate video-to-shop
matching. Fashion Focus presents a unified framework that can orientate the
consumers towards relevant product exhibitions during watching videos and help
the sellers to effectively deliver the products over search and recommendation.

该研究提出了一种名为 “时尚聚焦” 的多模态检索系统的创新演示，用于自动实现准确的视频到购物匹配，该系统结合图像、文本、交互等不同模态的特征，通过视频内容的结构化和多模态检索过程实现。