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- HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation138
This work presents HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm through a novel heterogeneous low-rank adaptation technique and a tailored hierarchical visual perception approach.
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- Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models41
A novel Gradient-RegulAted Meta-prompt learning (GRAM) framework that jointly meta-learns an efficient soft prompt initialization for better adaptation and a lightweight gradient regulating function for strong cross-domain generalizability in a meta-learning paradigm using only the unlabeled image-text pre-training data.
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- HERO: HiErarchical spatio-tempoRal reasOning with Contrastive Action Correspondence for End-to-End Video Object Grounding35
This paper introduces the weakly-supervised contrastive learning that classifies the video as action-consistent and action-independent frames relying on the video-caption action semantic correspondence and designs the hierarchical reasoning layers to decouple fully connected multi-head attention and remove the redundant interfering correlations.
- Semantic Alignment for Multimodal Large Language Models31
Semantic Alignment for Multi-modal large language models (SAM) is introduced, which aims to enhance the preservation of linking information for coherent analysis and align the semantics of different images before feeding them into LLM.
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- Multi-modal Action Chain Abductive Reasoning17
The proposed model significantly outperforms existing video-language models in terms of effectiveness on the newly created MAR dataset and design a novel symbolic module that can complete strict derivation of the progressive action chain layer by layer.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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