Search Results for author:
Found 8 papers, 6 papers with code
Reward Certification for Policy Smoothed Reinforcement Learning
Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it can be weakened by adversarial attacks.
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning
The experimental results show that our method produces meaningful guaranteed robustness for all models and environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime.
Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions
In this paper, we propose a synthetic-aware adverse weather robust algorithm for video stabilization that does not require real data and can be trained only on synthetic data.
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
The growth of videos in our digital age and the users' limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information.
Sparse Adversarial Video Attacks with Spatial Transformations
In recent years, a significant amount of research efforts concentrated on adversarial attacks on images, while adversarial video attacks have seldom been explored.
Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual Data
The rapid increase in the amount of published visual data and the limited time of users bring the demand for processing untrimmed videos to produce shorter versions that convey the same information.
Diverse Randomized Agents Vote to Win
We investigate the power of voting among diverse, randomized software agents.
