Search Results for author:
Found 4 papers, 3 papers with code
CATs++: Boosting Cost Aggregation with Convolutions and Transformers
Cost aggregation is a highly important process in image matching tasks, which aims to disambiguate the noisy matching scores.
Ranked #1 on
Semantic correspondence
on SPair-71k
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
In this paper, we address this limitation with a novel SSL framework for aggregating pseudo labels, called AggMatch, which refines initial pseudo labels by using different confident instances.
Cost Aggregation Is All You Need for Few-Shot Segmentation
We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.
Ranked #1 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
CATs: Cost Aggregation Transformers for Visual Correspondence
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.
Ranked #2 on
Semantic correspondence
on PF-PASCAL
