SOTAVerified

Affordance Detection

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.

Papers

Showing 110 of 23 papers

TitleStatusHype
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
Multi-label affordance mapping from egocentric visionCode1
Open-Vocabulary Affordance Detection in 3D Point CloudsCode1
Phrase-Based Affordance Detection via Cyclic Bilateral InteractionCode1
One-Shot Object Affordance Detection in the WildCode1
One-Shot Affordance DetectionCode1
3D AffordanceNet: A Benchmark for Visual Object Affordance UnderstandingCode1
Affordance detection with Dynamic-Tree Capsule NetworksCode0
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance DetectionCode0
Recognizing Object Affordances to Support Scene Reasoning for Manipulation TasksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.18Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.14Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.13Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.16Unverified