Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Diffusion models currently dominate the field of data-driven image synthesis with their
unparalleled scaling to large datasets. In this paper we identify and rectify several causes for …

Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images.
The idea is to synthesize a small number of synthetic data points that, when given to a …

Dataset distillation is the task of synthesizing a small dataset such that a model trained on
the synthetic set will match the test accuracy of the model trained on the full dataset. In this …

Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …

Dataset Condensation aims to condense a large dataset into a smaller one while
maintaining its ability to train a well-performing model, thus reducing the storage cost and …

Few-shot object detection (FSOD), with the aim to detect novel objects using very few
training examples, has recently attracted great research interest in the community. Metric …

Dataset distillation aims to synthesize small datasets with little information loss from original
large-scale ones for reducing storage and training costs. Recent state-of-the-art methods …