Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …

Weakly supervised segmentation methods using bounding box annotations focus on
obtaining a pixel-level mask from each box containing an object. Existing methods typically …

This paper presents a weakly supervised instance segmentation method that consumes
training data with tight bounding box annotations. The major difficulty lies in the uncertain …

In this paper, we introduce a novel learning scheme named weakly semi-supervised
instance segmentation (WSSIS) with point labels for budget-efficient and high-performance …

Weakly-supervised instance segmentation (WSIS) has been considered as a more
challenging task than weakly-supervised semantic segmentation (WSSS). Compared to …

Referring image segmentation (RIS) aims to localize the object in an image referred by a
natural language expression. Most previous studies learn RIS with a large-scale dataset …

Annotation burden has become one of the biggest barriers to semantic segmentation.
Approaches based on click-level annotations have therefore attracted increasing attention …

Instance segmentation seeks to identify and segment each object from images, which often
relies on a large number of dense annotations for model training. To alleviate this burden …

We present a new, simple yet effective approach to uplift video object detection. We observe
that prior works operate on instance-level feature aggregation that imminently neglects the …

Most state-of-the-art instance segmentation methods have to be trained on densely
annotated images. While difficult in general, this requirement is especially daunting for …