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

Saliency-based representation visualization (SRV)($ eg $, Grad-CAM) is one of the most
classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity …

Explainable AI aims to overcome the black-box nature of complex ML models like neural
networks by generating explanations for their predictions. Explanations often take the form of …

This paper introduces an efficient strategy to transform Large Language Models (LLMs) into
Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a …

The proliferation of face forgery techniques has raised significant concerns within society,
thereby motivating the development of face forgery detection methods. These methods aim …

Pre-trained large models for multimodal contrastive learning, such as CLIP, have been
widely recognized in the industry as highly susceptible to data-poisoned backdoor attacks …

The diagnosis of depression in individuals with Parkinson's Disease (PD) through the
utilization of multimodal fusion techniques represents a significant domain. The primary …

In-context learning (ICL) leverages in-context examples as prompts for the predictions of
Large Language Models (LLMs). These prompts play a crucial role in achieving strong …

Multimodal contrastive learning uses various data modalities to create high-quality features,
but its reliance on extensive data sources on the Internet makes it vulnerable to backdoor …

This work focuses on benchmarking the capabilities of vision large language models
(VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating …