Exploring Multimodal Vector Search

In today’s world, we are wittenssing a lot of advancement in terms of almost everything. Just a small example is searching anything on the internet. It happens quite a time that our search content has more than just text. For instance, it can also contain videos and images. These elements other than text can contain a lot of information which is not caught by the text.

By including other element into your search, you can increase the accuracy of the result and unlock new methods to search. These elements are often called as modalities.

Example of multimodal search include e-commerce and fashion which can have description, title, and displayed images. This info can help clarify the subject of the image if there are numerous items like tops and pants. The text can give important context to find the correct subject.

Multimodal Vector search is a search technology that allows us to search and retrieve various data like audios, videos, and images based on the content. It depends on the vector representation of data and advanced ML techniques to execute similarity-based searches.

Benefits of Muoltimodal Vector Search

There are various benefits of multimodal approach –

1. The multimodal representation of documents allows for using images, texts, or combination of both. This allows extended information to be received that is not given by either modality.
2. We can easily incorporate relevance feedback at document level to increase the quality of results.
3. We get editable and updatable meta data for documents without even re-indexing a huge amount of data.
4. By curating queries with additional contextual information, personalized and tailored results can be achieved for each query, all without necessitating additional models or intricate fine-tuning.
5. We can incorporate business logic into the seach by using natural language.
6. We can perform curation in natural language.

Key components and steps involved in multimodal vector search

Data Representation

The first and foremost step is to represent the given data in a numebrical format. This dat include audios, images, vidoes, text, etc. This is usually done by extricating features from the given data and converting them into HD vectors.

For instance, for images, you can use methods such as CNNs(Convulational neural networks) to extricate visual features. For audios, you can use spectrogram-based representations.

Vactorization

Second step is to transform the extracted features into vectors. These vectors seize the information and characteristics of the data in a numerical form.

Indexing

The vectors are now organized within a vector database, utilizing various data structures. These structures enable efficient similarity searches, employing methods such as locality-sensitive hashing, KD-trees, and advanced techniques like product quantization.

Querying

You can submit your query in the form of data. It will be converted into vector representation.

Similarity Search

Within this process, the system conducts a similarity search through the comparison of the query vector with the indexed vectors. It computes a similarity score for each indexed item when compared to the query, often employing common metrics like cosine similarity or Euclidean distance.

Ranking and Retrieval

After computing similarity scores, the system proceeds to rank the indexed items based on these scores in order to retrieve the highest-ranking items. These top-ranked items are then presented to the user as the search results.

Multimodal Search Use cases

Multimodal vector search possesses a diverse array of applications spanning numerous sectors due to its capacity to retrieve multimedia data grounded in content likeness. Below, we present notable instances of its utility:

1. Content-Based Image Discovery: Users can seek images akin to a provided query image, proving beneficial in e-commerce for locating visually analogous products and aiding image organization within content management systems.
2. Video Content Exploration: Multimodal search aids in pinpointing specific video segments within extensive libraries, leveraging both visual and audio content. This feature is advantageous for video editing and content management.
3. Music Recommendations: Tailoring song or audio track suggestions to users based on audio characteristics or mood. Multimodal search can also recommend songs aligned with a user’s input audio.
4. Medical Image Retrieval: In healthcare, practitioners and researchers can search for medical images, such as X-rays and MRIs, that closely resemble a reference image, facilitating diagnosis and research endeavors.
5. Reverse Image Tracing: Users can trace the origins or find related images by uploading an image, a valuable tool for verifying image sources on the internet.
6. Fashion and Style Harmonization: In the fashion industry, users can seek clothing or accessories that match items they possess or favor, fostering personalized fashion recommendations.
7. Product Design and Manufacturing: Engineers and designers can search for 3D models and CAD designs predicated on visual or structural resemblances, streamlining the design process.
8. Natural Language Processing (NLP) Coupled with Images: Enhancing search results by amalgamating text and image data. For instance, users can discover recipes by describing a dish in text and including an image of it.
9. Art and Cultural Heritage: Museums and art galleries can employ multimodal search to identify similar artworks in their collections or detect potential forgeries.
10. Audio Analysis and Music Mixing: Audio professionals can unearth audio clips that share characteristics like tempo or key, facilitating their use in music production or remixing.
11. Visual Content Moderation: Detecting and filtering inappropriate or harmful images and videos through the identification of visual patterns associated with inappropriate content.
12. Content Recommendations for Educational Platforms: Educational platforms can recommend learning materials, such as videos and articles, based on a student’s query content or study context.
13. Social Media Content Discovery: Users can discover visually or thematically analogous content on social media platforms, enhancing content exploration and engagement.
14. News and Media Analysis: Journalists and media enterprises can explore and scrutinize multimedia content to unearth trends, verify information, or collect user-generated content during events.
15. Geospatial and Satellite Imagery Analysis: Researchers and Geographic Information System (GIS) professionals can locate akin satellite or aerial images for tracking environmental shifts or scrutinizing geographical features.

These instances spotlight the manifold applications of multimodal vector search, fostering content retrieval, recommendation, and analysis across diverse industries and domains. Its adaptability underscores its significance in today’s data-centric landscape.

Frequently Asked Questions

What is the multimodal model?

Multimodal model is made of multiple unimodal neural networks. These neural networks process every input modality separately. For example, an audiovisual model can have 2 inimodal networks, one fo visual data and another for audio data. This singular processing is known as encoding.

What are the 5 multimodal?

1. Visual
2. Linguistic
3. Spatial
4. Audio
5. Gestural

How is it called multimodal?

Multimodality is incorporation of various literacies in a single medium. These multiple literacies contribute to the understanding of a composition. From the placement to the prganization of the content to mode of delivery creates meaning.

What is monomodal vs multimodal?

Multimodal texts combine images and words to produce results in a different way from monomdal texts. Monomodal texts relies only on words. They differ in representation as well as relationships between text producers and receivers.