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Found 8 papers, 5 papers with code
MEIA: Towards Realistic Multimodal Interaction and Manipulation for Embodied Robots
To overcome this limitation, we introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions.
Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs
Despite advancements in LLMs, knowledge-based reasoning remains a longstanding issue due to the fragility of knowledge recall and inference.
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning
We present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning tasks, such as VQA, image/video captioning, medical report generation, model generalization and robustness, etc.
VCD: Visual Causality Discovery for Cross-Modal Question Reasoning
Existing visual question reasoning methods usually fail to explicitly discover the inherent causal mechanism and ignore jointly modeling cross-modal event temporality and causality.
Cross-Modal Causal Intervention for Medical Report Generation
Medical report generation (MRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding medical reports according to the given radiology image.
Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology.
A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images
Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference.
Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images
The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics.
