Search Results for author:
Found 20 papers, 10 papers with code
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
Recently, stacked networks show powerful performance in Image Restoration, such as challenging motion deblurring problems.
BBScore: A Brownian Bridge Based Metric for Assessing Text Coherence
In summary, we present a novel Brownian bridge coherence metric capable of measuring both local and global text coherence, while circumventing the need for end-to-end model training.
Out-of-distribution Object Detection through Bayesian Uncertainty Estimation
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data.
MRET: Multi-resolution Transformer for Video Quality Assessment
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience.
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning
In this paper, we explore the performance of multi-agent reinforcement learning-based cross-layer congestion control algorithms and present cooperation performance of two agents, known as MACC (Multi-agent Congestion Control).
Exploring Semantic Relationships for Unpaired Image Captioning
To further improve the quality of captions generated by the model, we propose the Semantic Relationship Explorer, which explores the relationships between semantic concepts for better understanding of the image.
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances.
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning
We explore energy-based solutions for cooperative multi-agent reinforcement learning (MARL) using the idea of function factorization in centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Text as Neural Operator: Image Manipulation by Text Instruction
In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community.
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
Many tasks in computer vision and graphics fall within the framework of conditional image synthesis.
Approximate Feature Collisions in Neural Nets
Work on adversarial examples has shown that neural nets are surprisingly sensitive to adversarially chosen changes of small magnitude.
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.
Diverse Image Synthesis from Semantic Layouts via Conditional IMLE
Most existing methods for conditional image synthesis are only able to generate a single plausible image for any given input, or at best a fixed number of plausible images.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Imitation learning is a powerful paradigm for robot skill acquisition.
One-Shot Visual Imitation Learning via Meta-Learning
In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration.
Learning from the Hindsight Plan -- Episodic MPC Improvement
To bring the next real-world execution closer to the hindsight plan, our approach learns to re-shape the original cost function with the goal of satisfying the following property: short horizon planning (as realistic during real executions) with respect to the shaped cost should result in mimicking the hindsight plan.
PLATO: Policy Learning using Adaptive Trajectory Optimization
PLATO also maintains the MPC cost as an objective to avoid highly undesirable actions that would result from strictly following the learned policy before it has been fully trained.
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment.
