Looking Outside the Box to Ground Language in 3D Scenes. (arXiv:2112.08879v3 [cs.CV] UPDATED)

Most language grounding models learn to select the referred object from a
pool of object proposals provided by a pre-trained detector. This object
proposal bottleneck is limiting because an utterance may refer to visual
entities at various levels of granularity, such as the chair, the leg of a
chair, or the tip of the front leg of a chair, which may be missed by the
detector. Recently, MDETR introduced a language grounding model for 2D images
that do not have …

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