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Found 8 papers, 2 papers with code
Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
To that end, we examine the performance of learning through different combinations of self-supervised tasks on the facial expression recognition downstream task.
Action-based Contrastive Learning for Trajectory Prediction
To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings.
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields
In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly.
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data
In particular, we model the aligned data as a spatio-temporal Poisson point process.
A modular framework for object-based saccadic decisions in dynamic scenes
We model this active scene exploration as a sequential decision making process.
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation
Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding.
A Detailed Rubric for Motion Segmentation
The second is to report on new versions of three previously existing data sets that are compatible with this definition.
It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos
The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer, and even camouflage.
