A desirable data selection algorithm can efficiently choose the most informative samples to
maximize the utility of limited annotation budgets. However, current approaches …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Massive data is often considered essential for deep learning applications, but it also incurs
significant computational and infrastructural costs. Therefore, dataset pruning (DP) has …

The objective of Active Learning is to strategically label a subset of the dataset to maximize
performance within a predetermined labeling budget. In this study, we harness features …

Finetuning a pretrained vision model (PVM) is a common technique for learning downstream
vision tasks. The conventional finetuning process with the randomly sampled data points …

End-to-end differentiable learning for autonomous driving (AD) has recently become a
prominent paradigm. One main bottleneck lies in its voracious appetite for high-quality …

The primary strategy for mitigating lost productivity entails promptly, accurately, and
efficiently detecting plant pests. Although detection by humans can be useful in detecting …

The pretraining-finetuning paradigm has gained popularity in various computer vision tasks.
In this paradigm the emergence of active finetuning arises due to the abundance of large …

In this work, we investigate Active Vision Reinforcement Learning (ActiveVision-RL), where
an embodied agent simultaneously learns action policy for the task while also controlling its …