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Found 16 papers, 7 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Pontryagin Neural Operator for Solving Parametric General-Sum Differential Games
To address these challenges, we propose in this paper a Pontryagin-mode neural operator that outperforms existing state-of-the-art (SOTA) on safety performance across games with parametric state constraints.
Value Approximation for Two-Player General-Sum Differential Games with State Constraints
Solving Hamilton-Jacobi-Isaacs (HJI) PDEs numerically enables equilibrial feedback control in two-player differential games, yet faces the curse of dimensionality (CoD).
HAT: Hybrid Attention Transformer for Image Restoration
In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement.
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
In particular, we cluster the extensive degradation space to create a set of representative degradation cases, which serves as a comprehensive test set.
Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning
Our experiments show that such a simple approach can greatly reduce the occurrence of conflicting gradients in the remaining shared layers and achieve better performance, with only a slight increase in model parameters in many cases.
Adaptive Attitude Control for Foldable Quadrotors
Recent quadrotors have transcended conventional designs, emphasizing more on foldable and reconfigurable bodies.
Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Games
This paper investigates two potential solutions to this problem: a hybrid method that leverages both supervised Nash equilibria and the HJI PDE, and a value-hardening method where a sequence of HJIs are solved with a gradually hardening reward.
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds
The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world.
Event Detection Explorer: An Interactive Tool for Event Detection Exploration
ED Explorer consists of an interactive web application, an API, and an NLP toolkit, which can help both domain experts and non-experts to better understand the ED task.
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
Our study shows that existing methods severely suffer from catastrophic forgetting, a well-known problem in incremental learning, which is aggravated due to data scarcity and imbalance in the few-shot setting.
Ranked #7 on
Few-Shot Class-Incremental Learning
on mini-Imagenet
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics.
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior.
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics.
Ranked #1 on
Image Super-Resolution
on PIRM-test
How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving
While intelligence of autonomous vehicles (AVs) has significantly advanced in recent years, accidents involving AVs suggest that these autonomous systems lack gracefulness in driving when interacting with human drivers.
Robotics Computer Science and Game Theory
Classification of Neurological Gait Disorders Using Multi-task Feature Learning
An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait.
