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Found 6 papers, 3 papers with code
Graph Switching Dynamical Systems
Dynamical systems with complex behaviours, e. g. immune system cells interacting with a pathogen, are commonly modelled by splitting the behaviour into different regimes, or modes, each with simpler dynamics, and then learning the switching behaviour from one mode to another.
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting
To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem.
NFormer: Robust Person Re-identification with Neighbor Transformer
To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall.
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
To this end, we propose to untangle \emph{domain-invariant} crowd and \emph{domain-specific} background from crowd images and design a fine-grained domain adaption method for crowd counting.
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
In this way, we can transfer the original spatial labeling redundancy caused by individual similarities to effective supervision signals on the unlabeled regions.
Reciprocal Transformations for Unsupervised Video Object Segmentation
Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers.
