7 citations · 11 across the 34 of their papers we have counts for
24 papers · 1 filter
D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning
Evelyn Zhang, Fufu Yu, Aoqi Wu +5
Processing long visual token sequences poses a significant computational burden on Multimodal Large Language Models (MLLMs). While token pruning offers a path to acceleration, we f…
IPCV: Information-Preserving Compression for MLLM Visual Encoders
Yuan Chen, Zichen Wen, Yuzhou Wu +6
Multimodal Large Language Models (MLLMs) deliver strong vision-language performance but at high computational cost, driven by numerous visual tokens processed by the Vision Transfo…
VideoCompressa: Data-Efficient Video Understanding via Joint Temporal Compression and Spatial Reconstruction
Shaobo Wang, Tianle Niu, Runkang Yang +6
The scalability of video understanding models is increasingly limited by the prohibitive storage and computational costs of large-scale video datasets. While data synthesis has imp…
OmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learning
Hengrui Kang, Zhuangcheng Gu, Zhiyuan Zhao +4
Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, layout…
Diffusion LLM with Native Variable Generation Lengths: Let [EOS] Lead the Way
Yicun Yang, Cong Wang, Shaobo Wang +4
Diffusion-based large language models (dLLMs) have exhibited substantial potential for parallel text generation, which may enable more efficient generation compared to autoregressi…
AI for Service: Proactive Assistance with AI Glasses
Zichen Wen, Yiyu Wang, Chenfei Liao +10
In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-t…
AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs
Peize He, Zichen Wen, Yubo Wang +13
Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention () and with modeling lon…
ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Long Cui, Weiyun Wang, Jie Shao +6
Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Vis…
Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods
Chenfei Liao, Wensong Wang, Zichen Wen +10
Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly…
Efficient Multi-modal Large Language Models via Progressive Consistency Distillation
Zichen Wen, Shaobo Wang, Yufa Zhou +8
Visual tokens consume substantial computational resources in multi-modal large models (MLLMs), significantly compromising their efficiency. Recent works have attempted to improve e…
Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning
Shaobo Wang, Jiaming Wang, Jiajun Zhang +9
As supervised fine-tuning (SFT) evolves from a lightweight post-training step into a compute-intensive phase rivaling mid-training in scale, data efficiency has become critical for…
Variation-aware Vision Token Dropping for Faster Large Vision-Language Models
Junjie Chen, Xuyang Liu, Zichen Wen +3
Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding tasks. However, the increasing demand for high-resolution image and long-…
Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving
Minhao Xiong, Zichen Wen, Zhuangcheng Gu +9
Vision-Language Models (VLMs) have emerged as a promising paradigm in autonomous driving (AD), providing a unified framework for perception and decision-making. However, their real…
Thinking Inside the Mask: In-Place Prompting in Diffusion LLMs
Xiangqi Jin, Yuxuan Wang, Yifeng Gao +4
Despite large language models (LLMs) have achieved remarkable success, their prefix-only prompting paradigm and sequential generation process offer limited flexibility for bidirect…
LaPA: Length-Aware Prefix and Prompt Attention Augmentation for Long-Form Controllable Text Generation
Jiabing Yang, Yixiang Chen, Zichen Wen +8
Prefix-based methods have emerged as a promising paradigm for Controllable Text Generation (CTG) due to their parameter efficiency. However, while effective in short sequences, the…
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs
Zichen Wen, Jiashu Qu, Zhaorun Chen +13
Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater interactivity via parall…
EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
Yantai Yang, Yuhao Wang, Zichen Wen +5
Vision-Language-Action (VLA) models, particularly diffusion-based architectures, demonstrate transformative potential for embodied intelligence but are severely hampered by high co…
TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
Weiya Li, Junjie Chen, Bei Li +8
Machine translation has long been a central task in natural language processing. With the rapid advancement of large language models (LLMs), there has been remarkable progress in t…
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning
Shaobo Wang, Xiangqi Jin, Ziming Wang +8
Fine-tuning large language models (LLMs) on task-specific data is essential for their effective deployment. As dataset sizes grow, efficiently selecting optimal subsets for trainin…
Shifting AI Efficiency From Model-Centric to Data-Centric Compression
Xuyang Liu, Zichen Wen, Shaobo Wang +14
The advancement of large language models (LLMs) and multi-modal LLMs (MLLMs) has historically relied on scaling model parameters. However, as hardware limits constrain further mode…
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Hengrui Kang, Siwei Wen, Zichen Wen +8
The rapid advancements in generative technology have emerged as a double-edged sword. While offering powerful tools that enhance convenience, they also pose significant social conc…
Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
Siwei Wen, Junyan Ye, Peilin Feng +7
With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challe…
Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem?
Zichen Wen, Yifeng Gao, Weijia Li +2
Multimodal large language models (MLLMs) have shown remarkable performance for cross-modal understanding and generation, yet still suffer from severe inference costs. Recently, abu…
Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More
Zichen Wen, Yifeng Gao, Shaobo Wang +5
Vision tokens in multimodal large language models often dominate huge computational overhead due to their excessive length compared to linguistic modality. Abundant recent methods…