18 hours ago · Multimodal Large Language Models (MLLMs) integrate visual representations from images with linguistic embeddings from text, thereby enhancing the models' ...

6 hours ago · To learn the similarity and to assess the strength of the descriptor in matching the multimodal features, experimentation is conducted using seven distance ...

31 minutes ago · Multi-Modal Similarity: Combining textual and visual data can provide a more holistic view of product similarity, leading to better recommendations. By ...

14 hours ago · This approach involves training models to differentiate between similar and dissimilar audio samples by embedding them into a unified vector space. The core ...

5 hours ago · Learn about Mosaic AI Vector Search, Databricks' vector database solution, including what it is and how it works.

15 hours ago · The project will teach you several advanced ML techniques, such as count vectorizer (Bag of Words), cosine similarity, N-grams, and vector space model (VSM).

15 hours ago · This approach utilizes statistical methods to calculate similarity between item vectors and user vectors. CF methods can be categorized into two main types: ...

19 hours ago · Our study focuses on concreteness, a well-researched lexical-semantic variable, using it as a case study to examine the variability in visual representations.

18 hours ago · Oral Session 4A · DevBench: A multimodal developmental benchmark for language learning ; Oral Session 4B · CAT3D: Create Anything in 3D with Multi-View Diffusion ...

21 hours ago · The concept fringe-based concept-cognitive learning method considers both knowledge transfer and knowledge forgetting, which provides a more comprehensive ...