arXiv:2008.06285
Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States
원문 초록
Existing Human-Object Interaction (HOI) Detection approaches have achieved great progress on nonrare classes while rare HOI classes are still not well-detected. In this paper, we intend to apply human prior knowledge into the existing work. So we add human-labeled rules to PaStaNet and propose Rb-PaStaNet aimed at improving rare HOI classes detection. Our results show a certain improvement of the rare classes, while the non-rare classes and the overall improvement is more considerable.
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RoHOI: Robustness Benchmark for Human-Object Interaction Detection
Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection
VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions
Zero-Shot Temporal Interaction Localization for Egocentric Videos
Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations
Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors
토론
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