Abstract: Existing conditional video prediction approaches train a network from large databases and generalise to previously unseen data.

We explore for the first time predictive models that are domain-agnostic but data-specific. Our aim is to learn a model of a dynamic scene in the wild from a ...

Domain-agnostic Video Prediction from Motion Selective Kernels. Véronique Prinet (author). Da Li (presenter). International Conference on Image Processing ...

Domain-Agnostic Video Prediction from Motion Selective Kernels. September ... Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning.

We explore for the first time predictive models that are domain-agnostic but data-specific. Our aim is to learn a model of a dynamic scene in the wild from a ...

Note: A short version of this work is to appear under the title "Domain-agnostic video prediction from selective kernels", in ICIP 2019 (Taiwan, Sept) .

Domain-Agnostic Video Prediction from Motion Selective Kernels ... Japanese summary of the article(about several hundred characters). All summary is available on ...

The first survey on Domain Generalizable Person Re-identification (DG-ReID). We compare existing DG-ReID methods, highlighting their strengths and limitations.

Jun 1, 2025 · The predicted flows determine the sampling offsets for each pixel, while the learnable kernels capture residual motion and texture details.

Here, we show that hierarchical application of temporal prediction can capture how tuning properties change across at least two levels of the visual system.