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This paper presents a novel hierarchical alignment model (HAM) that learnsmulti-granularity visual and linguistic representations in an end-to-endmanner. We extract key points and proposal points to model 3D contexts andinstances, and propose point-language alignment with context modulation (PLACM)mechanism, which learns to gradually align word-level and sentence-levellinguistic embeddings with visual representations, while the modulation withthe visual context captures latent informative relationships. To furthercapture both global and local relationships, we propose a spatiallymulti-granular modeling scheme that applies PLACM to both global and localfields. Experimental results demonstrate the superiority of HAM, withvisualized results showing that it can dynamically model fine-grained visualand linguistic representations. HAM outperforms existing methods by asignificant margin and achieves state-of-the-art performance on two publiclyavailable datasets, and won the championship in ECCV 2022 ScanRefer challenge.Code is available at~\url{https://github.com/PPjmchen/HAM}.

Learning Point-Language Hierarchical Alignment for 3D Visual Grounding