RefAtomNet++: Advancing Referring Atomic Video Action Recognition using Multi-Trajectory Semantic Retrieval

Abstract:Who is being described, where are they, and what are they doing? Referring Atomic Video Action Recognition (RAVAR) answers these questions jointly by grounding a natural-language reference to a person and recognizing that person's fine-grained atomic actions in complex multi-person videos. Progress in RAVAR is constrained by limited benchmark scale and weak alignment between fine-grained linguistic and scene cues and temporally coherent visual cues. We address both challenges with a new dataset and model. We introduce RefAVA++, a large-scale dataset comprising 2,950,920$ frames, 75,111 annotated person instances, and 80 atomic action categories. Its references describe appearance and spatial attributes while deliberately omitting action labels, requiring models to infer actions directly from visual cues. We further propose RefAtomNet++, which models complementary semantics at the holistic-sentence, partial-keyword, and scene-attribute levels. Fine-grained semantic cues retrieve aligned visual tokens across time to construct trajectories, which are aggregated through Mamba-based state-space modeling and fused using multi-hierarchical semantic-aligned cross-attention. This design enables accurate joint person localization and multi-label atomic action recognition. Equipped with the InternVideo2.5 backbone, RefAtomNet++ achieves 50.39%/51.14% mIoU, 59.21%/59.55% mAP, and 73.89%/76.59% AUROC on RefAVA, and 49.49%/50.33% mIoU, 62.33%/61.90% mAP, and 76.06%/76.10% AUROC on RefAVA++ validation/test sets, respectively. The dataset and code are available at this https URL.

Extended version of ECCV 2024 paper arXiv:2407.01872. The dataset and code are available at this https URL

Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Robotics (cs.RO); Image and Video Processing (eess.IV)

arXiv:2510.16444 [cs.CV]

(or arXiv:2510.16444v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2510.16444 Focus to learn more arXiv-issued DOI via DataCite

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