Video Semantic Object Segmentation by Self-Adaptation of DCNN
This work addresses segmentation inconsistencies in videos for computer vision applications, representing an incremental advancement.
The paper tackles the label inconsistency problem in video semantic object segmentation by adapting a DCNN model using confidently-estimated frames from the video, achieving significant improvement over the original model and previous state-of-the-art methods.
This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods.