In a wide range of multimodal tasks, contrastive learning has become a particularly
appealing approach since it can successfully learn representations from abundant …

Abstract Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and
Florence, are trained on large private datasets of image-caption pairs and achieve superior …

Multimodal data pervades various domains, including healthcare, social media, and
transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal …

Contrastive Language-Image Pre-training (CLIP) has achieved excellent performance over
a wide range of tasks. However, the effectiveness of CLIP heavily relies on a substantial …

Recent advancements in multi-modal pre-training for 3D point clouds have demonstrated
promising results by aligning heterogeneous features across 3D shapes and their …

Understanding vehicles in images is important for various applications such as intelligent
transportation and self-driving system. Existing vehicle-centric works typically pre-train …

Humans perceive the world through multisensory integration, blending the information of
different modalities to adapt their behavior. Contrastive learning offers an appealing solution …

Building multisensory AI systems that learn from multiple sensory inputs such as text,
speech, video, real-world sensors, wearable devices, and medical data holds great promise …

This paper introduces 'SkeletonCLIP++', an extension of our prior work in human action
recognition, emphasizing the use of semantic information beyond traditional label-based …

This paper introduces a powerful encoder that transfers CLIPs capabilities to event-based
data, enhancing its utility and expanding its applicability across diverse domains. While …