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Found 10 papers, 0 papers with code
VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models
Our approach combines the off-the-shelf active tracking methods with VLMs' reasoning capabilities, deploying a fast visual policy for normal tracking and activating VLM reasoning only upon failure detection.
Hierarchical Instruction-aware Embodied Visual Tracking
User-Centric Embodied Visual Tracking (UC-EVT) presents a novel challenge for reinforcement learning-based models due to the substantial gap between high-level user instructions and low-level agent actions.
Clinical Inspired MRI Lesion Segmentation
Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications.
UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI
We introduce UnrealZoo, a rich collection of photo-realistic 3D virtual worlds built on Unreal Engine, designed to reflect the complexity and variability of the open worlds.
Enhanced MRI Representation via Cross-series Masking
In the training process, the cross-series representation is learned by utilizing the unmasked data to reconstruct the masked portions.
Autoregressive Sequence Modeling for 3D Medical Image Representation
By employing an autoregressive sequence modeling task, we predict the next visual token in the sequence, which allows our model to deeply understand and integrate the contextual information inherent in 3D medical images.
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
This transformation enables seamless integration of 2D and 3D data, and facilitates cross-dimensional self-supervised learning for 3D medical image analysis.
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL
The results show that our agent outperforms state-of-the-art methods in terms of sample efficiency, robustness to distractors, and generalization to unseen scenarios and targets.
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.
