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Found 5 papers, 4 papers with code
Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models
We introduces Crimson, a system that enhances the strategic reasoning capabilities of Large Language Models (LLMs) within the realm of cybersecurity.
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
In this paper, we formulate PAR as a vision-language fusion problem and fully exploit the relations between pedestrian images and attribute labels.
SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm
Then, a Transformer decoder is proposed to generate the human attributes by incorporating the visual features and attribute query tokens.
Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition.
Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition
The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning.
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