Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …

With the advent and widespread deployment of Multimodal Large Language Models
(MLLMs), the imperative to ensure their safety has become increasingly pronounced …

Vision-language models (VLMs) are typically composed of a vision encoder, eg CLIP, and a
language model (LM) that interprets the encoded features to solve downstream tasks …

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a
unique blend of opportunities and challenges. Although the field has expanded and is …

Large language models (LLMs) exhibit impressive capabilities across diverse tasks in
natural language processing. Nevertheless, challenges arise such as large model …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …

We introduce MobileVLM V2, a family of significantly improved vision language models
upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an …

Facial affective behavior analysis (FABA) is crucial for understanding human mental states
from images. However, traditional approaches primarily deploy models to discriminate …

In an era defined by the explosive growth of data and rapid technological advancements,
Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence …