15 citations · 25 across the 20 of their papers we have counts for
10 papers · 1 filter
Layer-Aware Position Embeddings for Visual Token Pruning in Multimodal Large Language Models
Yahong Wang, Zhangkai Ni, Juncheng Wu +3
Multimodal large language models (MLLMs) incur substantial computational overhead due to the reliance on hundreds of visual tokens to represent images. While token pruning has emer…
VisualClaw: A Real-Time, Personalized Agent for the Physical World
Haoqin Tu, Jianwen Chen, Zijun Wang +14
Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cos…
EntropyPrune: Matrix Entropy Guided Visual Token Pruning for Multimodal Large Language Models
Yahong Wang, Juncheng Wu, Zhangkai Ni +6
Multimodal large language models (MLLMs) incur substantial inference cost due to the processing of hundreds of visual tokens per image. Although token pruning has proven effective…
MedVLThinker: Simple Baselines for Multimodal Medical Reasoning
Xiaoke Huang, Juncheng Wu, Hui Liu +2
Large Reasoning Models (LRMs) have introduced a new paradigm in AI by enabling models to ``think before responding" via chain-of-thought reasoning. However, the absence of open and…
SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning
Yunfei Xie, Yuxuan Cheng, Juncheng Wu +3
Recent advancements in adapting vision-language pre-training models like CLIP for person re-identification (ReID) tasks often rely on complex adapter design or modality-specific tu…
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning
Suhao Yu, Haojin Wang, Juncheng Wu +9
Real-world clinical practice demands multi-image comparative reasoning, yet current medical benchmarks remain limited to single-frame interpretation. We present MedFrameQA, the fir…
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine
Yunfei Xie, Ce Zhou, Lang Gao +8
This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities with multigranular annotati…
Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance
Jiawei Mao, Juncheng Wu, Yuyin Zhou +2
There are many excellent solutions in image restoration.However, most methods require on training separate models to restore images with different types of degradation.Although exi…
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
Juncheng Wu, Zhangkai Ni, Hanli Wang +3
Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method…
Misalignment-Robust Frequency Distribution Loss for Image Transformation
Zhangkai Ni, Juncheng Wu, Zian Wang +3
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely a…