3260 papers • 126 benchmarks • 313 datasets
Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. Image source: Object-Based Affordances Detection with Convolutional Neural Networks and Dense Conditional Random Fields Unlike other visual or physical properties that mainly describe the object alone, affordances indicate functional interactions of object parts with humans.
(Image credit: Papersgraph)
These leaderboards are used to track progress in affordance-detection-2
Use these libraries to find affordance-detection-2 models and implementations
No subtasks available.
To perceive affordances from a vision-language perspective, and consider the challenging phrase-based affordance detection task, a cyclic bilateral consistency enhancement network (CBCE-Net) is proposed to align language and vision features in a progressive manner.
The experimental results on the public datasets show that the 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.
An affordance transfer learning approach is introduced to jointly detect HOIs with novel object and recognize affordances, and is capable of inferring the affordances of novel objects from known affordance representations.
A One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images to empower robots with this ability in unseen scenarios.
This paper develops and evaluates a novel method that allows for the detection of affordances in a scalable and multiple-instance manner on visually recovered pointclouds based on highly parallelizable, one-shot learning that is fast in commodity hardware.
A One-Shot Affordance Detection Network (OSAD-Net) is devised that firstly estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images and builds a large-scale purpose-driven affordance dataset v2.
It is found that SD is harder than OD and that tailored SD methods need to be developed for addressing this problem, and a number of appropriately designed experiments are conducted towards an in-depth study of the behavior of the SD problem.
Adding a benchmark result helps the community track progress.