12 papers
See Fair, Speak Truth: Equitable Attention Improves Grounding and Reduces Hallucination in Vision-Language Alignment
Mohammad Anas Azeez, Ankan Deria, Zohaib Hasan Siddiqui +5
Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding is disproportionately drawn t…
CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning
Ankan Deria, Komal Kumar, Xilin He +4
Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive enc…
SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia
Zhixiang Lu, Chong Zhang, Yulong Li +5
The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language mod…
MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images
Ankan Deria, Komal Kumar, Adinath Madhavrao Dukre +3
Multimodal large language models have advanced rapidly, but their adoption in medicine is constrained by limited domain coverage, imperfect modality alignment, and insufficient gro…
DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning
Xiwei Liu, Yulong Li, Feilong Tang +1
Adapting Large Multimodal Models (LMMs) to real-world scenarios poses the dual challenges of learning from sequential data streams while handling frequent modality incompleteness,…
ClinCoT: Clinical-Aware Visual Chain-of-Thought for Medical Vision Language Models
Xiwei Liu, Yulong Li, Xinlin Zhuang +5
Medical Vision-Language Models have shown promising potential in clinical decision support, yet they remain prone to factual hallucinations due to insufficient grounding in localiz…