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This effectively limits the performance and generalization capabilities of existing video segmentation methods.
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #23 on Semantic Segmentation on Cityscapes test
This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.
Ranked #1 on Visual Object Tracking on YouTube-VOS
Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation.
This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks.
Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higher-order relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation.
For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence.