Abstract: Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional ...
The diagram below shows the detailed architecture of the vS-Graphs framework, highlighting the key threads and their interactions. Modules with a light gray background are inherited directly from the ...
To address the degradation of visual-language (VL) representations during VLA supervised fine-tuning (SFT), we introduce Visual Representation Alignment. During SFT, we pull a VLA’s visual tokens ...
Abstract: The detection of network threats remains a fundamental task in cyberspace defense. Graphs are capable of capturing rich structural information and retaining content information. They are ...
CLIP is one of the most important multimodal foundational models today. What powers CLIP’s capabilities? The rich supervision signals provided by natural language, the carrier of human knowledge, ...
CLIP is one of the most important multimodal foundational models today, aligning visual and textual signals into a shared feature space using a simple contrastive learning loss on large-scale ...
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