INT: Instance-Specific Negative Mining for Task-Generic Promptable
  Segmentation
Authors: name: Jian Hu, name: Zixu Cheng, name: Shaogang Gong

Task-generic promptable image segmentation aims to achieve segmentation of
diverse samples under a single task description by utilizing only one
task-generic prompt. Current methods leverage the generalization capabilities
of Vision-Language Models (VLMs) to infer instance-specific prompts from these
task-generic prompts in order to guide the segmentation process. However, when
VLMs struggle to generalise to some image instances, predicting
instance-specific prompts becomes poor. To solve this problem, we introduce
Instance-specific Negative Mining for Task-Generic
Promptable Segmentation (INT). The key idea of INT is to adaptively
reduce the influence of irrelevant (negative) prior knowledge whilst to
increase the use the most plausible prior knowledge, selected by negative
mining with higher contrast, in order to optimise instance-specific prompts
generation. Specifically, INT consists of two components: (1) instance-specific
prompt generation, which progressively fliters out incorrect information in
prompt generation; (2) semantic mask generation, which ensures each image
instance segmentation matches correctly the semantics of the instance-specific
prompts. INT is validated on six datasets, including camouflaged objects and
medical images, demonstrating its effectiveness, robustness and scalability.