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Found 6 papers, 2 papers with code
Discovering Latent Classes for Semi-Supervised Semantic Segmentation
On unlabeled images, we predict a probability map for latent classes and use it as a supervision signal to learn semantic segmentation.
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation
They do not require additional curation as it is the case for the clean class tags used by current weakly supervised approaches and they provide textual context for the classes present in an image.
What Object Should I Use? - Task Driven Object Detection
When humans have to solve everyday tasks, they simply pick the objects that are most suitable.
Two Stream 3D Semantic Scene Completion
In this work, we propose a two stream approach that leverages depth information and semantic information, which is inferred from the RGB image, for this task.
Ranked #7 on
3D Semantic Scene Completion
on SemanticKITTI
3D Semantic Scene Completion
Vocal Bursts Valence Prediction
Adaptive Binarization for Weakly Supervised Affordance Segmentation
The concept of affordance is important to understand the relevance of object parts for a certain functional interaction.
Weakly Supervised Affordance Detection
Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications.
