INTRA: Interaction Relationship-aware Weakly Supervised Affordance
  Grounding
Authors: name: Ji Ha Jang, name: Hoigi Seo, name: Se Young Chun

Affordance denotes the potential interactions inherent in objects. The
perception of affordance can enable intelligent agents to navigate and interact
with new environments efficiently. Weakly supervised affordance grounding
teaches agents the concept of affordance without costly pixel-level
annotations, but with exocentric images. Although recent advances in weakly
supervised affordance grounding yielded promising results, there remain
challenges including the requirement for paired exocentric and egocentric image
dataset, and the complexity in grounding diverse affordances for a single
object. To address them, we propose INTeraction Relationship-aware weakly
supervised Affordance grounding (INTRA). Unlike prior arts, INTRA recasts this
problem as representation learning to identify unique features of interactions
through contrastive learning with exocentric images only, eliminating the need
for paired datasets. Moreover, we leverage vision-language model embeddings for
performing affordance grounding flexibly with any text, designing
text-conditioned affordance map generation to reflect interaction relationship
for contrastive learning and enhancing robustness with our text synonym
augmentation. Our method outperformed prior arts on diverse datasets such as
AGD20K, IIT-AFF, CAD and UMD. Additionally, experimental results demonstrate
that our method has remarkable domain scalability for synthesized images /
illustrations and is capable of performing affordance grounding for novel
interactions and objects.