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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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- Unsupervised Domain Adaptation for Video Object Grounding with Cascaded Debiasing Learning9
This paper addresses the Unsupervised Domain Adaptation (UDA) for the dense frame prediction task - Video Object Grounding and measures Grounding Coordinate Offset based on the semantic distance of the model's prediction across domains, based on which it alleviate source-domain bias in the target model through adversarial learning.
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- Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration8
A universal on-device Multi-modal Model Adaptation framework is introduced, revolutionizing on-device model adaptation by striking a balance between efficiency and effectiveness, and represents a pioneering solution for on-Device Multi-modal Model Adaptation (DMMA).
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- Hierarchical Debiasing and Noisy Correction for Cross-domain Video Tube Retrieval6
The unsupervised domain adaptation model via Hierarchical dEbiAsing and noisy corRecTion (HEART) for cross-domain video tube retrieval is proposed, which contains two characteristic modules: Layered Feature Debiasing and Pseudo Label Refinement.
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- BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval6
A novel Bottom-up crOss-modal Semantic compoSition (BOSS) with Hybrid Counterfactual Training framework, which sheds new light on the CIR task by studying it from two previously overlooked perspectives.
- Consistent and Invariant Generalization Learning for Short-video Misinformation Detection5
A new DOmain generalization model via ConsisTency and invariance learning for shORt-video misinformation detection (named DOCTOR), which contains two characteristic modules: the cross-modal feature interpolation to map multiple modalities into a shared space and the interpolation distillation to synchronize multi-modal learning.
- Few-Shot Incremental Learning via Foreground Aggregation and Knowledge Transfer for Audio-Visual Semantic Segmentation5
Few-Shot Incremental Learning (FSIL) is introduced to the AVSS task, which seeks to seamlessly integrate new classes with limited incremental samples while preserving the knowledge of old classes.
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- ART: rule bAsed futuRe-inference deducTion2
This work introduces rule b A sed futu R e-inference deduc T ion ( ART), which aims at de-ducing the correct future event based on the visual phenomenon and the rule-based premises, along with an explanation of the reasoning process.
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- ExpTalk: Diverse Emotional Expression via Adaptive Disentanglement and Refined Alignment for Speech-Driven 3D Facial Animation1
ExTalk is proposed, which first applies an Adaptive Disentanglement Variational Autoencoder to decouple facial expression aligned with spoken language and emotions of speech through contrastive learning, and a Refined Alignment Diffusion is employed to iteratively refine the decoupled facial expression priors through diffusion-based perturbations.
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- Head-wise Modality Specialization within MLLMs for Robust Fake News Detection under Missing Modality–
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- Cross-modal Observation Hypothesis Inference–
A large-scale text-video dataset that contains 39,796 meticulously annotated hypothesis inference examples and auxiliary commonsense knowledge for key video characters is proposed, and a strong baseline, COINNet, is designed, which features two perspectives: aligning temporally displaced textual observations with target videos via transformer-based multi-task learning, and inferring the action flow with non-parametric graph-based inference grounded in graph theory.
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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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