1 citations · 1 across the 7 of their papers we have counts for
7 papers
Counterfactual Tests for Measuring Chain-of-Thought Faithfulness in Visual Language Models
Bayar Menzat, Maximilian Süss, Ruizhi Wang +3
Chain-of-thought (CoT) may often look plausible, yet it may not faithfully reflect the model's decision-making process. While methods for measuring the faithfulness of CoTs for tex…
Vision Hopfield Memory Networks for Image Recognition
Jianfeng Wang, Amine M'Charrak, Luk Koska +6
Recent vision backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress on image recognition. Despite their empirical success, th…
Prototype Transformer: Towards Language Model Architectures Interpretable by Design
Yordan Yordanov, Matteo Forasassi, Bayar Menzat +6
While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and…
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network
Ruizhi Wang, Xiangtao Wang, Jie Zhou +2
In clinical scenarios, multiple medical images with different views are usually generated simultaneously, and these images have high semantic consistency. However, most existing me…
AMLP: Adjustable Masking Lesion Patches for Self-Supervised Medical Image Segmentation
Xiangtao Wang, Ruizhi Wang, Thomas Lukasiewicz +1
Self-supervised masked image modeling (MIM) methods have shown promising performances on analyzing natural images. However, directly applying such methods to medical image segmenta…
MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation
Ruizhi Wang, Xiangtao Wang, Zhenghua Xu +3
In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency. However, the existing medical r…
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation
Xiangtao Wang, Ruizhi Wang, Biao Tian +5
Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling…