Search Results for author:
Found 5 papers, 4 papers with code
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution
The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts.
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates.
Ranked #1 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
Multi-scale Matching Networks for Semantic Correspondence
We follow the coarse-to-fine matching strategy and build a top-down feature and matching enhancement scheme that is coupled with the multi-scale hierarchy of deep convolutional neural networks.
Ranked #11 on
Semantic correspondence
on SPair-71k
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
To address the problem brought by the environment, we propose a Meta Soft Hierarchical reinforcement learning framework (MeSH), in which each low-level sub-policy focuses on a specific sub-task respectively and high-level policy automatically learns to utilize low-level sub-policies through meta-gradients.
Hierarchical Reinforcement Learning
Meta Reinforcement Learning
+2
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i. e., high-level agent and low-level agent.
Hierarchical Reinforcement Learning
Recommendation Systems
+2
