Search Results for author:
Found 5 papers, 4 papers with code
Enhancing Representations through Heterogeneous Self-Supervised Learning
Thus, we propose Heterogeneous Self-Supervised Learning (HSSL), which enforces a base model to learn from an auxiliary head whose architecture is heterogeneous from the base model.
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further.
Ranked #2 on
Instance Segmentation
on COCO 2017 val
(AP metric)
SERE: Exploring Feature Self-relation for Self-supervised Transformer
Specifically, instead of conducting self-supervised learning solely on feature embeddings from multiple views, we utilize the feature self-relations, i. e., spatial/channel self-relations, for self-supervised learning.
Ranked #2 on
Semantic Segmentation
on ImageNet-S
Large-scale Unsupervised Semantic Segmentation
In this work, we propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to help the research progress.
Ranked #1 on
Unsupervised Semantic Segmentation
on ImageNet-S-300
Global2Local: Efficient Structure Search for Video Action Segmentation
Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further.
Ranked #20 on
Action Segmentation
on Breakfast
