AttriPrompt: Class Attribute-aware Prompt Tuning for Vision-Language Model [0.03%]
AttriPrompt：面向视觉语言模型的类属性感知提示调优
Yuling Su,Xueliang Liu,Zhen Huang et al.
Yuling Su et al.
Prompt tuning has proven to be an effective alternative for fine-tuning the pre-trained vision-language models (VLMs) to downstream tasks. Among existing approaches, class-shared prompts learn a unified prompt shared across all classes, whi...
Domain-Adaptive Mamba for Cross-Scene Hyperspectral Image Classification [0.03%]
跨场景高光谱图像分类的领域自适应马 MI法
Puhong Duan,Shiyu Jin,Xiaotian Lu et al.
Puhong Duan et al.
Cross-scene hyperspectral image classification aims to identify a new scene in target domain via learned knowledge from source domain using limited training samples. Existing cross-scene alignment approaches focus on aligning the global fea...
Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability [0.03%]
基于特征混合和元学习的频率探索以提升对抗样本的迁移性
Juanjuan Weng,Zhiming Luo,Shaozi Li
Juanjuan Weng
Recent studies have shown that Deep Neural Networks (DNNs) are susceptible to adversarial attacks, with frequency-domain analysis underscoring the significance of high-frequency components in influencing model predictions. Conversely, targe...
Domain-Complementary Prior with Fine-Grained Feedback for Scene Text Image Super-Resolution [0.03%]
基于细粒度反馈的域互补先验场景文本超分辨率方法
Shen Zhang,Yang Li,Pengwen Dai et al.
Shen Zhang et al.
Enhancing the resolution of scene text images is a critical preprocessing step that can substantially improve the accuracy of downstream text recognition in low-quality images. Existing methods primarily rely on auxiliary text features to g...
Hao Chen,Haoran Zhou,Yunshu Zhang et al.
Hao Chen et al.
In the RGB-D vision community, extensive research has been focused on designing multi-modal learning strategies and fusion structures. However, the complementary and fusion mechanisms in RGB-D models remain a black box. In this paper, we pr...
ThinkMatter: Panoramic-Aware Instructional Semantics for Monocular Vision-and-Language Navigation [0.03%]
面向单目视觉语言导航的全景感知指令语义思考事项
Guangzhao Dai,Shuo Wang,Hao Zhao et al.
Guangzhao Dai et al.
Vision-and-Language Navigation in continuous environments (VLN-CE) requires an embodied robot to navigate the target destination following the natural language instruction. Most existing methods use panoramic RGB-D cameras for 360° observa...
A Few-Shot Class Incremental Learning Method Using Graph Neural Networks [0.03%]
一种基于图神经网络的Few-shot类增量学习方法
Yuqian Ma,Youfa Liu,Bo Du
Yuqian Ma
Few-shot class incremental learning (FSCIL) aims to continuously learn new classes from limited training samples while retaining previously acquired knowledge. Existing approaches are not fully capable of balancing stability and plasticity ...
Domain-aware Adversarial Domain Augmentation Network for Hyperspectral Image Classification [0.03%]
领域感知对抗领域增强网络的高光谱影像分类方法
Yi Huang,Jiangtao Peng,Weiwei Sun et al.
Yi Huang et al.
Classifying hyperspectral remote sensing images across different scenes has recently emerged as a significant challenge. When only historical labeled images (source domain, SD) are available, it is crucial to leverage these images effective...
BP-NeRF: End-to-End Neural Radiance Fields for Sparse Images without Camera Pose in Complex Scenes [0.03%]
基于稀疏图像的复杂场景下的端到端神经辐射场方法
Yaru Qiu,Guoxia Wu,Yuanyuan Sun
Yaru Qiu
Synthesizing novel perspectives of complex scenes in high quality using sparse image sequences, especially for those without camera poses, is a challenging task. The key to enhancing accuracy in such scenarios lies in sufficient prior knowl...
Deep G-PCC Geometry Preprocessing via Joint Optimization with a Differentiable Codec Surrogate for Enhanced Compression Efficiency [0.03%]
基于可微编码器代理的联合优化的深度G-PCC几何预处理以增强压缩效率
Wanhao Ma,Wei Zhang,Shuai Wan et al.
Wanhao Ma et al.
Geometry-based point cloud compression (G-PCC), an international standard designed by MPEG, provides a generic framework for compressing diverse types of point clouds while ensuring interoperability across applications and devices. However,...