Existing computer vision systems can compete with humans in understanding the visible
parts of objects, but still fall far short of humans when it comes to depicting the invisible parts …

Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To
reduce the annotation pressure, we focus on a challenging task named zero-shot semantic …

Amodal object segmentation is a challenging task that involves segmenting both visible and
occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to …

Abstract Amodal Instance Segmentation (AIS) endeavors to accurately deduce complete
object shapes that are partially or fully occluded. However, the inherent ill-posed nature of …

Almost all existing amodal segmentation methods make the inferences of occluded regions
by using features corresponding to the whole image. This is against the human's amodal …

Zero-shot semantic segmentation, which is developed to segment unseen categories, has
attracted increasing attention due to its strong practicability. Previous approaches usually …

Monocular object detection and tracking have improved drastically in recent years, but rely
on a key assumption: that objects are visible to the camera. Many offline tracking …

We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …

Amodal instance segmentation aims to predict the complete mask of the occluded instance,
including both visible and invisible regions. Existing 2D AIS methods learn and predict the …

Amodal instance segmentation aims to infer the amodal mask, including both the visible part
and occluded part of each object instance. Predicting the occluded parts is challenging …