AffordanceNet: An End-to-End Deep Learning Approach for
Object Affordance Detection
Thanh-Toan Do*, Anh Nguyen*, Ian Reid
Abstract:
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 object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet is well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code and trained models will be made available.
Paper: PDF
Code: Github
Dataset: IIT-AFF
Related Publications:
Anh Nguyen, Dimitrios Kanoulas, Darwin G. Caldwell, Nikos Tsagarakis
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
Detecting Object Affordances with Convolutional Neural Networks
Anh Nguyen, Dimitrios Kanoulas, Darwin G. Caldwell, Nikos Tsagarakis
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
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