The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection
Open accessAbstract
Computers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among different object instances, and can only recognize a handful of types by exhaustively training individual detectors for all possible relationships. To capture such global interdependency, we propose a deep Variation-structured Re-inforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image. First, a directed semantic action graph is built using language priors to provide a rich and compact representation of semantic correlations between object categories, predicates, and attributes. Next, we use a variation-structured traversal over the action graph to construct a small, adaptive action set for each step based on the current state and historical actions. In particular, an ambiguity-aware object mining scheme is used to resolve semantic ambiguity among object categories that the object detector fails to distinguish. We then make sequential predictions using a deep RL framework, incorporating global context cues and semantic embeddings of previously extracted phrases in the state vector. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome dataset validate the superiority of VRL, which can achieve significantly better detection results on datasets involving thousands of relationship and attribute types. We also demonstrate that VRL is able to predict unseen types embedded in our action graph by learning correlations on shared graph nodes.
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Cites
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Cited by
Neural Motifs: Scene Graph Parsing with Global Context
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Visual Translation Embedding Network for Visual Relation Detection
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3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
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Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs
Ji, J., Krishna, R., Fei-Fei, L., & Niebles, J. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01025 · Cited by 303
Metadata
- Authors
- Liang, X., Lee, L., & Xing, E. P.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4408-4417
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963650529
- Cited by
- 243
- References
- 13
- FWCI
- 12.78
- Type
- conference paper
- Language
- en
- Open access
- green
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