7 citations · 7 across the 11 of their papers we have counts for
12 papers
Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder
Zidan Wang, Yaqian Li, Xiaokai Zhang +3
CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a lightweight route to refine CLIP, but…
Trajectory-Level Speculative Decoding for Diffusion Language Models
Tianxiang Pan, Baitao Gong, Mo Guang +5
Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to single-token generation under low…
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…
Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering
Lin Fan, Yafei Ou, Zhipeng Deng +8
Chain-of-thought (CoT) reasoning has advanced medical visual question answering (VQA), yet most existing CoT rationales are free-form and fail to capture the structured reasoning p…
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…
A Survey for Foundation Models in Autonomous Driving
Haoxiang Gao, Zhongruo Wang, Yaqian Li +3
The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD). T…
GearV: A Two-Gear Hypervisor for Mixed-Criticality IoT Systems
Kaiwen Long, Chong Xing, Yuebin Qi +10
This paper presents GearV, a two-gear lightweight hypervisor architecture to address the some known challenges. By dividing hypervisor into some partitions, and dividing scheduling…