The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is directly supervised by the contrastive loss computed upon the contrastive items (CIs), which are sets of anchor/positive/negative embeddings. Recent online VIS methods leverage CIs sourced from one reference frame only, which we argue is insufficient for learning highly discriminative embeddings. Intuitively, a possible strategy to enhance CIs is replicating the inference phase during training. To this end, we propose a simple yet effective training strategy, called Consistent Training for Online VIS (CTVIS), which devotes to aligning the training and inference pipelines in terms of building CIs. Specifically, CTVIS constructs CIs by referring inference the momentum-averaged embedding and the memory bank storage mechanisms, and adding noise to the relevant embeddings. Such an extension allows a reliable comparison between embeddings of current instances and the stable representations of historical instances, thereby conferring an advantage in modeling VIS challenges such as occlusion, re-identification, and deformation. Empirically, CTVIS outstrips the SOTA VIS models by up to +5.0 points on three VIS benchmarks, including YTVIS19 (55.1% AP), YTVIS21 (50.1% AP) and OVIS (35.5% AP). Furthermore, we find that pseudo-videos transformed from images can train robust models surpassing fully-supervised ones.

Incorporating ventilation images into radiotherapy plans to treat lung cancer could reduce the incidence of debilitating radiation-induced lung injuries, such as radiation pneumonitis and radiation fibrosis. Specifically, ventilation imaging can be used to adapt radiation treatment plans to reduce the dose to high-functioning lung. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) scans are the gold standard of ventilation imaging. However, these modalities are not always readily available and the cost of such exams may be prohibitive. As such, researchers are investigating the feasibility of alternatives such as MR or CT ventilation imaging.