Facial micro-expressions are rapid involuntary facial expressions which reveal suppressed
affect. To the best knowledge of the authors, there is no previous work that successfully …

With recent progress in graphics, it has become more tractable to train models on synthetic
images, potentially avoiding the need for expensive annotations. However, learning from …

The mainstream paradigm behind continual learning has been to adapt the model parameters
to non-stationary data distributions, where catastrophic forgetting is the central challenge. …

We aim at constructing a high performance model for defect detection that detects unknown
anomalous patterns of an image without anomalous data. To this end, we propose a two-…

The objective of this work is human pose estimation in videos, where multiple frames are
available. We investigate a ConvNet architecture that is able to benefit from temporal context by …

Multi-horizon forecasting often contains a complex mix of inputs – including static (ie time-invariant)
covariates, known future inputs, and other exogenous time series that are only …

We propose a novel high-performance and interpretable canonical deep tabular data
learning architecture, TabNet. TabNet uses sequential attention to choose which features to …

Continual learning aims to enable a single model to learn a sequence of tasks without
catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past …

Deploying large language models (LLMs) is challenging because they are memory inefficient
and compute-intensive for practical applications. In reaction, researchers train smaller task-…

Micro-expressions are short, involuntary facial expressions which reveal hidden emotions.
Micro-expressions are important for understanding humans' deceitful behavior. Psychologists …