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Found 28 papers, 12 papers with code
DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations
Weather radar data synthesis can fill in data for areas where ground observations are missing.
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning
Finally, we adopt a visual prompting question-answering paradigm for the training of unified weather understanding tasks.
Understanding Layer Significance in LLM Alignment
We propose a novel approach to identify the important layers for LLM alignment (ILA).
PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling
By rethinking the blurriness in precipitation nowcasting as a blur kernel acting on predictions, we propose an unsupervised postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth.
A Preliminary Exploration Towards General Image Restoration
Existing deep models, tailored for specific individual image restoration tasks, often fall short in effectively addressing these challenges.
UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation
In this work, we present UniFashion, a unified framework that simultaneously tackles the challenges of multimodal generation and retrieval tasks within the fashion domain, integrating image generation with retrieval tasks and text generation tasks.
Learning A Low-Level Vision Generalist via Visual Task Prompt
In addition, these methods are sensitive to prompt image content and often struggle with low-frequency information processing.
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
Based on the degradation similarity, GRIDS divides restoration tasks into one of the optimal groups, where tasks within the same group are highly correlated.
Small Aerial Target Detection for Airborne Infrared Detection Systems using LightGBM and Trajectory Constraints
A simple and effective small aerial target detection method for airborne infrared detection system using light gradient boosting model (LightGBM) and trajectory constraints is proposed in this article.
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
This paper proposes a physics-AI hybrid model (i. e., WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset.
DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance.
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.
TFCounter:Polishing Gems for Training-Free Object Counting
Object counting is a challenging task with broad application prospects in security surveillance, traffic management, and disease diagnosis.
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 the current state-of-the-art hybrid PINN model 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 #8 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.
