2 citations · 2 across the 12 of their papers we have counts for
13 papers
HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enhancement
Yiyang Cai, Nan Chen, Rongchang Xie +8
Human-object centric video personalization (HOCVP) is a core task within subject-driven video generation. However, existing methods suffer from two key limitations. First, most app…
DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation
Nan Chen, Yiyang Cai, Rongchang Xie +7
Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Open domain S2V mainly involves two scenarios: in-domain, which r…
Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE
Yangming Shi, Shixiang Zhu, Tao Shen +14
We present Mamoda2.5, a unified AR-Diffusion framework that seamlessly integrates multimodal understanding and generation within a single architecture. To efficiently enhance the m…
MammothModa2: A Unified AR-Diffusion Framework for Multimodal Understanding and Generation
Tao Shen, Xin Wan, Taicai Chen +10
Unified multimodal models aim to integrate understanding and generation within a single framework, yet bridging the gap between discrete semantic reasoning and high-fidelity visual…
OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs
Feng Chen, Yefei He, Shaoxuan He +9
Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge t…
ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking
Lequan Lin, Dai Shi, Andi Han +7
Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large lan…
Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs
Ziye Chen, Yiqun Duan, Riheng Zhu +2
Personalized multiple clustering aims to generate diverse partitions of a dataset based on different user-specific aspects, rather than a single clustering. It has recently drawn r…
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
Yun Li, Zhe Liu, Yajing Kong +6
Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approac…
Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
Feng Chen, Chenhui Gou, Jing Liu +6
As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a…
Frame-Voyager: Learning to Query Frames for Video Large Language Models
Sicheng Yu, Chengkai Jin, Huanyu Wang +9
Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it…
Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption Utilization
Minyi Zhao, Jie Wang, Zhaoyang Li +3
Recent studies have shown that Vision Language Large Models (VLLMs) may output content not relevant to the input images. This problem, called the hallucination phenomenon, undoubte…
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment
Hongyu Hu, Tiancheng Lin, Jie Wang +2
Large-scale vision-language models (VLMs), e.g., CLIP, learn broad visual concepts from tedious training data, showing superb generalization ability. Amount of prompt learning meth…
CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning
Hongyu Hu, Jiyuan Zhang, Minyi Zhao +1
Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM). Nevertheless, these Vision-Lang…