5 papers · 1 filter
Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning
Haoyu Wang, Haonan Wang, Yuyan Chen +5
In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile. Our analysis reveals that…
Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification
Lexiang Hu, Youze Xue, Dian Li +2
Multimodal embeddings serve as a bridge for aligning vision and language, with the two primary implementations -- CLIP-based and MLLM-based embedding models -- both limited to capt…
Improve Multi-Modal Embedding Learning via Explicit Hard Negative Gradient Amplifying
Youze Xue, Dian Li, Gang Liu
With the rapid advancement of multi-modal large language models (MLLMs) in recent years, the foundational Contrastive Language-Image Pretraining (CLIP) framework has been successfu…
Critique Before Thinking: Mitigating Hallucination through Rationale-Augmented Instruction Tuning
Zexian Yang, Dian Li, Dayan Wu +2
Despite significant advancements in multimodal reasoning tasks, existing Large Vision-Language Models (LVLMs) are prone to producing visually ungrounded responses when interpreting…
Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Fanrui Zhang, Dian Li, Qiang Zhang +6
The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large…