33 citations · 48 across the 7 of their papers we have counts for
7 papers
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
Xiaofeng Han, Shunpeng Chen, Zenghuang Fu +9
Robot vision has greatly benefited from advancements in multimodal fusion techniques and vision-language models (VLMs). We adopt a task-oriented perspective to systematically revie…
SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation
Rongtao Xu, Changwei Wang, Jiguang Zhang +3
Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task b…
Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic Segmentation
Wenhao Xu, Rongtao Xu, Changwei Wang +4
Recently, CLIP has found practical utility in the domain of pixel-level zero-shot segmentation tasks. The present landscape features two-stage methodologies beset by issues such as…
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation
Rongtao Xu, Changwei Wang, Jiaxi Sun +3
Efficiently training accurate deep models for weakly supervised semantic segmentation (WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS methods…
MTLDesc: Looking Wider to Describe Better
Changwei Wang, Rongtao Xu, Yuyang Zhang +4
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of…
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Changwei Wang, Rongtao Xu, Shibiao Xu +3
Accurate lung lesion segmentation from Computed Tomography (CT) images is crucial to the analysis and diagnosis of lung diseases such as COVID-19 and lung cancer. However, the smal…
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
Chengcheng Ma, Baoyuan Wu, Shibiao Xu +4
Adversarial examples have been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples, based on the assumption…