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Recent years have seen a growing interest in Scene Graph Generation (SGG), acomprehensive visual scene understanding task that aims to predict entityrelationships using a relation encoder-decoder pipeline stacked on top of anobject encoder-decoder backbone. Unfortunately, current SGG methods suffer froman information loss regarding the entities local-level cues during the relationencoding process. To mitigate this, we introduce the Vision rElationTransfOrmer (VETO), consisting of a novel local-level entity relation encoder.We further observe that many existing SGG methods claim to be unbiased, but arestill biased towards either head or tail classes. To overcome this bias, weintroduce a Mutually Exclusive ExperT (MEET) learning strategy that capturesimportant relation features without bias towards head or tail classes.Experimental results on the VG and GQA datasets demonstrate that VETO + MEETboosts the predictive performance by up to 47 percentage over the state of theart while being 10 times smaller.

Vision Relation Transformer for Unbiased Scene Graph Generation