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Found 5 papers, 4 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
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)
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest.
Deep Matching Prior: Test-Time Optimization for Dense Correspondence
Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model.
Ranked #1 on
Dense Pixel Correspondence Estimation
on HPatches
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
