  Driven by large data trained segmentation models, such as SAM , research in
one-shot segmentation has experienced significant advancements. Recent
contributions like PerSAM and MATCHER , presented at ICLR 2024, utilize a
similar approach by leveraging SAM with one or a few reference images to
generate high quality segmentation masks for target images. Specifically, they
utilize raw encoded features to compute cosine similarity between patches
within reference and target images along the channel dimension, effectively
generating prompt points or boxes for the target images a technique referred to
as the matching strategy. However, relying solely on raw features might
introduce biases and lack robustness for such a complex task. To address this
concern, we delve into the issues of feature interaction and uneven
distribution inherent in raw feature based matching. In this paper, we propose
a simple and training-free method to enhance the validity and robustness of the
matching strategy at no additional computational cost (NubbleDrop). The core
concept involves randomly dropping feature channels (setting them to zero)
during the matching process, thereby preventing models from being influenced by
channels containing deceptive information. This technique mimics discarding
pathological nubbles, and it can be seamlessly applied to other similarity
computing scenarios. We conduct a comprehensive set of experiments, considering
a wide range of factors, to demonstrate the effectiveness and validity of our
proposed method. Our results showcase the significant improvements achieved
through this simmple and straightforward approach.
