---

layout: paper

id: 35

slides_live_id: 38930703

rocket_id: ool-paper-35

meeting_url:

authors: "Thomas Kipf"

camera_ready: true

cmt_id: -1

kind: oral

session_id: 0

session_title: "Invited Talk"

title: "Attentive Grouping and Graph Neural Networks for Object-Centric Learning"

abstract: "To enable explicit representation of objects in neural architectures, a core challenge lies in defining a mapping from input features (e.g., an image encoded by a CNN) to a set of abstract object representations. In this talk, I will discuss how attention mechanisms can be used in an iterative, competitive fashion to (a) efficiently group visual features into object slots and (b) segment temporal representations. I will further highlight how graph neural networks can be utilized to learn about interactions between objects and how object-centric models can be trained in a self-supervised fashion using contrastive losses."

track: invited

live: false

video_file_url: none

youtube_url: none

---