Cross Attention
Cross-attention is a mechanism that allows neural networks to relate information from different parts of an input, such as relating words in a sentence to pixels in an image, or aligning audio and video streams. Current research focuses on improving the efficiency and effectiveness of cross-attention in various applications, including image generation, video processing, and multimodal learning, often employing transformer architectures or state-space models like Mamba. This attention mechanism is proving crucial for enhancing performance in tasks requiring the integration of diverse data sources, leading to improvements in areas such as scene change detection, style transfer, and multimodal emotion recognition. The resulting advancements have significant implications for various fields, including computer vision, natural language processing, and healthcare.
Papers
OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer
Junjie Gao, Qiujie Dong, Ruian Wang, Shuangmin Chen, Shiqing Xin, Changhe Tu, Wenping Wang
MoEmo Vision Transformer: Integrating Cross-Attention and Movement Vectors in 3D Pose Estimation for HRI Emotion Detection
David C. Jeong, Tianma Shen, Hongji Liu, Raghav Kapoor, Casey Nguyen, Song Liu, Christopher A. Kitts
Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck
Jayesh Songara, Shivam Pande, Shabnam Choudhury, Biplab Banerjee, Rajbabu Velmurugan
AV-SepFormer: Cross-Attention SepFormer for Audio-Visual Target Speaker Extraction
Jiuxin Lin, Xinyu Cai, Heinrich Dinkel, Jun Chen, Zhiyong Yan, Yongqing Wang, Junbo Zhang, Zhiyong Wu, Yujun Wang, Helen Meng