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Found 4 papers, 3 papers with code
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Our model is well-suited to obtain global semantic region information while also accommodates local spatial boundary characteristics simultaneously.
Video Annotation for Visual Tracking via Selection and Refinement
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve.
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations.
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
To capture richer perceptions and extract more comprehensive video representations, in this paper we propose a novel framework named Trigeminal Transformers (TMT) for video-based person Re-ID.
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