Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of data representation, e.g. balancing data distribution or learning unbiased models and representations, ignoring the mechanism that how humans accomplish this task. Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode. To this end, we propose a novel  Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree URLanize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a hierarchical loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships while progressively eliminating the interference of irrelevant ones . The loss is model-independent and can be applied to various SGG models without extra supervision. The proposed CogTree loss consistently boosts the performance of several state-of-the-art models on the Visual Genome benchmark.
Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work , we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships , making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree URLanize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships . The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models . The code is available at: URL
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data representation, e.g.
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mechanism that how humans accomplish this task. Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning
correlations among the biased classes. In this work
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learning
automatically building
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of the highly-biased relationships
from the biased predictions
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classes
relationships
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classes
relationships
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debiasing
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hierarchical
debiasing
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while progressively eliminating the interference of irrelevant ones
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model-independent and can be applied to various SGG models without extra supervision. The proposed CogTree loss consistently boosts
model-agnostic and consistently boosting
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on the Visual Genome benchmark.
. The code is available at: URL