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 …

The ability to understand the ways to interact with objects from visual cues, aka visual
affordance, is essential to vision-guided robotic research. This involves categorizing …

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 …

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 …

We present a novel method for learning and predicting the affordances of an object based
on its physical and visual attributes. Affordance prediction is a key task in autonomous robot …

Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …

Contrary to the vast literature in modeling, perceiving, and understanding agent-object (eg,
human-object, hand-object, robot-object) interaction in computer vision and robotics, very …

Detecting objects in aerial images usually faces two major challenges:(1) detecting difficult
targets (eg, small objects, objects that are interfered by the background, or various …

Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is
a fundamental yet practical problem in many machine vision applications, such as robot …