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Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL
We evaluate our tracker on several high-fidelity environments with challenging situations, such as distraction and occlusion.
Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations
Accurate identification of breast masses is crucial in diagnosing breast cancer; however, it can be challenging due to their small size and being camouflaged in surrounding normal glands.
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification
To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains.
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