Localizing functional regions of objects or affordances is an important aspect of scene
understanding and relevant for many robotics applications. In this work, we introduce a pixel …

Object affordance detection aims to identify, locate and segment the functional regions of
objects, so that robots can understand and manipulate objects like humans. The affordance …

The concept of affordance is important to understand the relevance of object parts for a
certain functional interac-tion. Affordance types generalize across object categories and are …

We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple
objects and their affordances from RGB images. Our AffordanceNet has two branches: an …

Affordance detection refers to identifying the potential action possibilities of objects in an
image, which is a crucial ability for robot perception and manipulation. To empower robots …

Accurate affordance detection and segmentation with pixel precision is an important piece in
many complex systems based on interactions, such as robots and assitive devices. We …

Affordance detection refers to identifying the potential action possibilities of objects in an
image, which is an important ability for robot perception and manipulation. To empower …

Object affordance detection, which aims to understand functional attributes of objects, is of
great significance for an autonomous robot to achieve a humanoid object manipulation. In …

We revisit the notion of object affordances, an idea that speaks to an object's functional
properties more than its class label. We study the problem of spatially localizing affordances …

This letter investigates the addition of keypoint detections to a deep network affordance
segmentation pipeline. The intent is to better interpret the functionality of object parts from a …