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Found 7 papers, 6 papers with code
Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition
Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt a pre-trained foundation model CLIP to extract the visual features.
Pre-training on High Definition X-ray Images: An Experimental Study
Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e. g., 224 $\times$ 224).
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.
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.
