10 papers
Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction
Chaoqun He, Mingyang Xiang, Yingjing Xu +5
Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming inputs and respond at approp…
Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification
Tianshu Zhang, Yan Wang, Ji Qi +1
Spatio-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries. While recent vision-language mod…
VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation
Bo Li, Ronghao Chen, Ningyuan Deng +3
Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within social media and e-commerce doma…
Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution
Tian Qin, Junzhe Chen, Yuqing Shi +3
Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding methods mitigate this problem…
MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation
Bo Li, Ningyuan Deng, Tianyu Dong +3
Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual information within images cruci…
DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models
Guanzhi Deng, Bo Li, Ronghao Chen +5
Mixture-of-Experts (MoE) has become a prominent paradigm for scaling Large Language Models (LLMs). Parameter-efficient fine-tuning methods, such as LoRA, are widely adopted to adap…