Visual object segmentation based on temporal and linguistic cues - Yang, Z (2022)
The aspiration of computer vision is to enable computers to make sense of a world, one that exists in a vast sea of colorful pixels reflecting what humans see every day. Central to this goal perhaps, is the discovery of object representations from these pixels: clusters of pixels representing the things from big to small that constitute our physical world and the interactions with which form our daily life. Visual object segmentation is a subject that deals with this task. It concerns the prediction of pixel-wise masks that delineate objects of interest from visual data. Different types of evidence have been leveraged for segmenting out objects (according to the application), e.g., contours, saliency, motion, extreme points, and pre-defined semantic categories, among others. In this thesis, we investigate the exploitation of temporal correspondences and natural language descriptions for visual object segmentation, motivated by their omnipresence and efficacy, and contribute new methods that improve the state of the art for the relevant applications.