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Found 19 papers, 5 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.
Assessing and Understanding Creativity in Large Language Models
This paper aims to establish an efficient framework for assessing the level of creativity in LLMs.
Context Shift Reduction for Offline Meta-Reinforcement Learning
In this paper, we propose a novel approach called Context Shift Reduction for OMRL (CSRO) to address the context shift problem with only offline datasets.
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning
Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world.
Online Prototype Alignment for Few-shot Policy Transfer
Domain adaptation in reinforcement learning (RL) mainly deals with the changes of observation when transferring the policy to a new environment.
AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions
In recent years, videos and images in 720p (HD), 1080p (FHD) and 4K (UHD) resolution have become more popular for display devices such as TVs, mobile phones and VR.
Conceptual Reinforcement Learning for Language-Conditioned Tasks
Recently, the language-conditioned policy is proposed to facilitate policy transfer through learning the joint representation of observation and text that catches the compact and invariant information across environments.
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution
In this paper, considering the characteristics of compressed videos, we propose a Codec Information Assisted Framework (CIAF) to boost and accelerate recurrent VSR models for compressed videos.
Object-Category Aware Reinforcement Learning
Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL.
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Learning Controllable Elements Oriented Representations for Reinforcement Learning
Deep Reinforcement Learning (deep RL) has been successfully applied to solve various decision-making problems in recent years.
Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment
In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices.
DWM: A Decomposable Winograd Method for Convolution Acceleration
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images
As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET).
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions.
