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Found 9 papers, 3 papers with code
Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style
Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage.
Planning and Rendering: Towards End-to-End Product Poster Generation
At the planning stage, we propose a PlanNet to generate the layout of the product and other visual components considering both the appearance features of the product and semantic features of the text, which improves the diversity and rationality of the layouts.
Relation-Aware Diffusion Model for Controllable Poster Layout Generation
To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2.
SoK: On the Semantic AI Security in Autonomous Driving
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical.
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
