2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019
DOI: 10.1109/cvpr.2019.00645
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Synthesizing Environment-Aware Activities via Activity Sketches

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Cited by 18 publications
(29 citation statements)
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“…The development of VirtualHome environment [32] enables such possibility. However, relevant works [32,21] rely on human-annotated data and perform supervised training from scratch. Due to the lack of rich world knowledge, these models can only generate action plans given detailed instructions of how to act or video demonstrations.…”
Section: Related Workmentioning
confidence: 99%
“…The development of VirtualHome environment [32] enables such possibility. However, relevant works [32,21] rely on human-annotated data and perform supervised training from scratch. Due to the lack of rich world knowledge, these models can only generate action plans given detailed instructions of how to act or video demonstrations.…”
Section: Related Workmentioning
confidence: 99%
“…The VirtualHome dataset [17,23] contains activities that people do at home. For each activity, there are different descriptions on how to perform them.…”
Section: Household Datasetmentioning
confidence: 99%
“…Furthermore, our framework can recommend sequences of actions on how to perform a human scaled task, like "How can I make a sandwich?". Our framework is based on a domain-specific ontology that we have developed which contains knowledge from the VirtualHome dataset [17,23]. The ontology is built in OWL [19] and the Knowledge Base (KB) can be easily extended by adding new instances of objects, actions, and activities.…”
Section: Introductionmentioning
confidence: 99%
“…Puig et al [14] build a data base of symbolic programs constituting high-level tasks in a home by using human subject instructing a virtual agent in a simulation environment. Liao et al [15] use the corpus to learn translations of domain independent task sketches to executable programs in the agent's physical context. Shridhar et al [16] take a similar approach by collecting natural language corpora describing high-level tasks and learn to associate instructions to spatial attention over the scene.…”
Section: Related Workmentioning
confidence: 99%
“…Given the current world state S (as a graph) and the goal description Λ g , our neural model estimates the likelihood p(t | S, Λ g ) over candidate tool objects t ∈ τ in the environment. We build on the ResActGraph model by Liao et al [15] as the baseline and extend the model to our problem setup. Figure 4 presents the final TOOLNET model.…”
Section: Learning To Predict Tool Usementioning
confidence: 99%