Infants possess a remarkable ability to rapidly learn and process visual inputs. As an infant's
mobility increases, so does the variety and dynamics of their visual inputs. Is this change in …

Existing paradigms of pushing the state of the art require exponentially more training data in
many fields. Coreset selection seeks to mitigate this growing demand by identifying the most …

Most curriculum learning methods require an approach to sort the data samples by difficulty,
which is often cumbersome to perform. In this work, we propose a novel curriculum learning …

Crowd counting is one of the important and challenging research topics in computer vision.
In recent years, with the rapid development of deep learning, the model architectures …

Quantifying the number of individuals in images or videos to estimate crowd density is a
challenging yet crucial task with significant implications for fields such as urban planning …

Most curriculum learning methods require an approach to sort the data samples by difficulty,
which is often cumbersome to perform. In this work, we propose a novel curriculum learning …

The success of modern deep learning is attributed to two key elements: huge amounts of
training data and large model sizes. Where a vast amount of data allow the model to learn …

Counting individuals in highly crowded environments, characterized by thousands of
people, has garnered significant attention in recent years, due to the high number of vertical …

This paper improves upon existing data pruning methods for image classification by
introducing a novel pruning metric and pruning procedure based on importance sampling …

Recent advances in deep learning techniques have achieved remarkable performance in
several computer vision problems. A notably intuitive technique called Curriculum Learning …