Visual Search Technology in Tourism E-Commerce
In tourism e-commerce, visual search technology has emerged as a transformative and impactful tool, fundamentally altering the way consumers explore and discover products online. This technology utilizes advanced computer vision and machine learning algorithms to analyze and comprehend the visual content of images. By extracting key features, patterns, and characteristics from product images, visual search systems can accurately identify and match items, providing users with relevant and visually similar results. This capability is particularly beneficial in scenarios where users find it challenging to articulate their search intent through text or encounter language barriers.
However, the challenges associated with this task remain substantial. Firstly, employing deep learning methods for visual search typically demands substantial computational resources, potentially posing challenges for some small to medium-sized tourism e-commerce platforms. Secondly, the processing of image data may involve user privacy concerns, necessitating effective measures on the part of tourism e-commerce platforms to safeguard the privacy and security of user data. Overall, the application of visual search technology in tourism e-commerce continues to evolve, offering users and merchants a more intelligent and convenient interactive experience, with the potential to become a key driving force in the future development of the tourism e-commerce industry.
Visual Retrieval Based on Multi-Modal Data
The visual retrieval method based on multi-modal data enriches the information representation of the visual retrieval system by simultaneously considering various data modalities such as images, text, and audio, thereby providing users with more comprehensive and accurate retrieval results. Common multi-modal visual retrieval methods achieve correlated learning between different data modalities by simultaneously training multiple modal feature extractors. This approach, through information sharing, captures the inter-modality correlations more effectively, enhancing the performance of the retrieval system (Ye & Zhao, 2023; Ye et al., 2023; Yuan et al., 2022).
In terms of application effectiveness, multi-modal visual retrieval based on diverse data modalities integrates information from images, text, audio, and more, offering a more comprehensive visual understanding that assists in meeting users' retrieval needs more accurately. Furthermore, the consideration of multi-modal data aids in modeling semantic relationships between different data modalities, improving the retrieval system's understanding of complex semantic information and thereby enhancing the relevance of retrieval results. Lastly, multi-modal learning contributes to improving the system's generalization ability to new data, especially when dealing with limited samples or scarce modalities, enabling better adaptation to diverse data distributions.
However, the use of deep learning and joint learning methods may involve higher computational complexity, posing challenges in environments with limited computing resources. An additional challenge lies in the laborious and time-consuming process of annotating multi-modal data, particularly when dealing with intricate semantic relationships between modalities, which further increases the difficulty of annotation.