4 papers
Boosting Active Learning with Knowledge Transfer
Tianyang Wang, Xi Xiao, Gaofei Chen +3
Uncertainty estimation is at the core of Active Learning (AL). Most existing methods resort to complex auxiliary models and advanced training fashions to estimate uncertainty for u…
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
Tianyang Wang, Xi Xiao, Gaofei Chen +4
Segment Anything Model (SAM) has demonstrated impressive zero-shot segmentation capabilities across natural image domains, but it struggles to generalize to the unique challenges o…
ReviBranch: Deep Reinforcement Learning for Branch-and-Bound with Revived Trajectories
Dou Jiabao, Nie Jiayi, Yihang Cheng +5
The Branch-and-bound (B&B) algorithm is the main solver for Mixed Integer Linear Programs (MILPs), where the selection of branching variable is essential to computational efficienc…
Deep Active Learning with Manifold-preserving Trajectory Sampling
Yingrui Ji, Vijaya Sindhoori Kaza, Nishanth Artham +1
Active learning (AL) is for optimizing the selection of unlabeled data for annotation (labeling), aiming to enhance model performance while minimizing labeling effort. The key ques…