Delving deep into many-to-many attention for few-shot video object segmentation

• Main Authors: CHEN, Haoxin, WU, Hanjie, ZHAO, Nanxuan, REN, Sucheng, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Agent network; Breakings; Linear spaces; Linear time; Many to many; Novel domain; Object information; Query video; Single frames; Video objects segmentations; Graphics and Human Computer Interfaces; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8527; https://ink.library.smu.edu.sg/context/sis_research/article/9530/viewcontent/Delving_deep_into_many_to_many_attention_for_few_shot_video_object_segmentation.pdf
• Institution: Singapore Management University

This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object inform...