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 11–20 of 23 papers

TitleStatusHype
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
3D AffordanceNet: A Benchmark for Visual Object Affordance UnderstandingCode1
Recognizing Object Affordances to Support Scene Reasoning for Manipulation TasksCode0
Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor—0
Scene Understanding for Autonomous Manipulation with Deep Learning—0
What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detectionCode0
Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling—0
Visual Affordance and Function Understanding: A Survey—0
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance DetectionCode0
Weakly Supervised Affordance DetectionCode0
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Benchmark Results

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