6 citations · 9 across the 10 of their papers we have counts for
7 papers · 1 filter
Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning
Yihong Huang, Fei Ma, Yihua Shao +4
Vision token pruning has proven to be an effective acceleration technique for the efficient Vision Language Model (VLM). However, existing pruning methods demonstrate excellent per…
SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification
Yu Bai, Zitong Yu, Haowen Tian +11
We propose Spatial-Aware Correlated Multiple Instance Learning (SAC-MIL) for performing WSI classification. SAC-MIL consists of a positional encoding module to encode position info…
Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning
Ye Zhu, Yunan Wang, Zitong Yu
Multimodal news contains a wealth of information and is easily affected by deepfake modeling attacks. To combat the latest image and text generation methods, we present a new Multi…
EPE-P: Evidence-based Parameter-efficient Prompting for Multimodal Learning with Missing Modalities
Zhe Chen, Xun Lin, Yawen Cui +1
Missing modalities are a common challenge in real-world multimodal learning scenarios, occurring during both training and testing. Existing methods for managing missing modalities…
TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model
Yuhao Wang, Chao Hao, Yawen Cui +4
The vision-language modeling capability of multi-modal large language models has attracted wide attention from the community. However, in medical domain, radiology report generatio…
Difflare: Removing Image Lens Flare with Latent Diffusion Model
Tianwen Zhou, Qihao Duan, Zitong Yu
The recovery of high-quality images from images corrupted by lens flare presents a significant challenge in low-level vision. Contemporary deep learning methods frequently entail t…
FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba
Xinyu Xie, Yawen Cui, Tao Tan +2
Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive, detail-rich single image for downstream vision tasks. Existing m…