Vision Language Models are Confused Tourists

The introduction of MultiPriv marks a significant advancement in the evaluation of individual-level privacy reasoning within Vision-Language Models (VLMs). This benchmark addresses the inadequacies of current privacy assessments, which primarily focus on privacy perception rather than the ability of VLMs to link distributed information and construct individual profiles. The framework includes a novel bilingual multimodal dataset that features synthetic individual profiles linked to sensitive attributes.

A new framework called MMT-ARD has been proposed to enhance the robustness of Vision-Language Models (VLMs) through a Multimodal Multi-Teacher Adversarial Distillation approach. This method addresses the limitations of traditional single-teacher distillation by incorporating a dual-teacher knowledge fusion architecture, which optimizes both clean feature preservation and robust feature enhancement.

A novel approach called Vision-align-to-Language integrated Knowledge Graph (VaLiK) has been proposed to enhance reasoning in Large Language Models (LLMs) by constructing Multimodal Knowledge Graphs (MMKGs) without the need for manual annotations. This method aims to address challenges such as incomplete knowledge and hallucination artifacts that LLMs face due to the limitations of traditional Knowledge Graphs (KGs).

SPEAR-1 has been introduced as a significant advancement in the field of robotic foundation models, aiming to enhance the generalization capabilities of robots across diverse environments and tasks. This initiative addresses the limitations of existing models that primarily rely on 2D image-language tasks, which do not adequately support 3D spatial reasoning necessary for effective robotic control.

Recent research has examined whether Vision-Language Models (VLMs) comprehend visual persuasion, which influences human attitudes and decisions. A new dataset was created for binary persuasiveness judgment, introducing a taxonomy of Visual Persuasive Factors (VPFs) that includes various levels of visual cues. The analysis indicates that VLMs tend to overestimate high persuasiveness and struggle with low/mid-level features, while high-level semantic alignment is a strong predictor of human judgment.

VLA-Pruner has been introduced as a novel method for token pruning in Vision-Language-Action (VLA) models, addressing the inefficiencies of existing approaches that focus solely on semantic salience. This method aims to enhance real-time deployment of VLA models by retaining critical information necessary for action generation while discarding redundant visual tokens.