  Visual affordance grounding aims to segment all possible interaction regions
between people and objects from an image/video, which is beneficial for many
applications, such as robot grasping and action recognition. However, existing
methods mainly rely on the appearance feature of the objects to segment each
region of the image, which face the following two problems: (i) there are
multiple possible regions in an object that people interact with; and (ii)
there are multiple possible human interactions in the same object region. To
address these problems, we propose a Hand-aided Affordance Grounding Network
(HAGNet) that leverages the aided clues provided by the position and action of
the hand in demonstration videos to eliminate the multiple possibilities and
better locate the interaction regions in the object. Specifically, HAG-Net has
a dual-branch structure to process the demonstration video and object image.
For the video branch, we introduce hand-aided attention to enhance the region
around the hand in each video frame and then use the LSTM network to aggregate
the action features. For the object branch, we introduce a semantic enhancement
module (SEM) to make the network focus on different parts of the object
according to the action classes and utilize a distillation loss to align the
output features of the object branch with that of the video branch and transfer
the knowledge in the video branch to the object branch. Quantitative and
qualitative evaluations on two challenging datasets show that our method has
achieved stateof-the-art results for affordance grounding. The source code will
be made available to the public.
