10 citations · 18 across the 5 of their papers we have counts for
5 papers · 1 filter
Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim +1
This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. In the context of dense matching, many works benefit f…
GMConv: Modulating Effective Receptive Fields for Convolutional Kernels
Qi Chen, Chao Li, Jia Ning +2
In convolutional neural networks, the convolutions are conventionally performed using a square kernel with a fixed N N receptive field (RF). However, what matters most to…
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam +2
This paper presents a novel cost aggregation network, called Volumetric Aggregation with Transformers (VAT), for few-shot segmentation. The use of transformers can benefit correlat…
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance
Zhihang Zhong, Xiao Sun, Zhirong Wu +3
We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without consi…
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
Yinghao Xu, Fangyun Wei, Xiao Sun +5
Semi-supervised action recognition is a challenging but important task due to the high cost of data annotation. A common approach to this problem is to assign unlabeled data with p…