26 citations · 29 across the 9 of their papers we have counts for
9 papers
MS-Resampler: Multi-Scope Visual Resampling for Efficient Multimodal LLMs
Zhongyang Li, Yaqian Li, Faming Fang +6
Multimodal large language models (MLLMs) typically employ resampling-based projectors to transform dense visual features into a compact token sequence for language modeling. Most e…
SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs
Zi-Hao Bo, Yaqian Li, Anzhou Hou +6
Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert routing remains under-explor…
LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models
Rinyoichi Takezoe, Yaqian Li, Zihao Bo +3
Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning, but they also impose significant computational burdens due t…
QMoP: Query Guided Mixture-of-Projector for Efficient Visual Token Compression
Zhongyang Li, Yaqian Li, Faming Fang +6
Multimodal large language models suffer from severe computational and memory bottlenecks, as the number of visual tokens far exceeds that of textual tokens. While recent methods em…
ITO: Multi-View Alignment and Training-Time Fusion for Image-Text Pretraining
Hanpeng Liu, Yaqian Li, Zidan Wang +6
Image--text contrastive pretraining has become a dominant paradigm for visual representation learning, yet existing methods often yield representations that remain partially organi…
iGVLM: Dynamic Instruction-Guided Vision Encoding for Question-Aware Multimodal Understanding
Hanpeng Liu, Yaqian Li, Zidan Wang +5
Despite the success of Large Vision--Language Models (LVLMs), most existing architectures suffer from a representation bottleneck: they rely on static, instruction-agnostic vision…
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
Yanxiao Zhao, Yaqian Li, Zihao Bo +6
Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities. We prese…
Deep Active Learning for Computer Vision: Past and Future
Rinyoichi Takezoe, Xu Liu, Shunan Mao +4
As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given…
ALBench: A Framework for Evaluating Active Learning in Object Detection
Zhanpeng Feng, Shiliang Zhang, Rinyoichi Takezoe +5
Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architect…