arXiv:2606. 10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding.
By Zhiyuan Zhu, Yixuan Chen, Yiwen Shao, Wenxiang Guo, Changhao Pan, Yu Zhang, Yuxiang Wang, Wei Liu, Houhua Zhang, Chengkuan Zeng, Wenbo Cheng, Yunxi Liu, Rui Yang, Steve Yves, Liefeng Bo, Zhou Zhao
arXiv:2607. 15265v1 Announce Type: cross Abstract: We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language.
By Mingfei Chen, Zijun Cui, Ruoke Zhang, Hyeonggon Ryu, Eli Shlizerman
arXiv:2608. 16285v1 Announce Type: cross Abstract: Audio-Visual Segmentation (AVS) is a fundamental task in multimodal perception that performs pixel-level segmentation of sounding objects in videos by leveraging both visual and audio cues.
By Zhaojin Fu, Yuyang Hong, Qi Yang, Zili Wang, Kun Ding, Shiming Xiang, Bin Fan
The paper introduces CTAN, a Cycle-Temporal Attention Network for audio‑visual embodied navigation. It proposes an Audio‑Visual Reconstruction Cross‑Attention module that uses bidirectional cycle‑consistency to strengthen spatial semantics across visual and acoustic modalities, and a Temporal Cross‑Modal Memory to fuse real‑time multimodal features with historical context. Experiments on Replica and Matterport3D show that CTAN outperforms prior methods in success rate, SPL, and scene navigation accuracy.
By Teng Liu, Yinfeng Yu
arXiv:2402. 01591v4 Announce Type: replace-cross Abstract: Spatial sound reasoning is a fundamental human skill, enabling us to navigate and interpret our surroundings based on sound.
By Zhisheng Zheng, Puyuan Peng, Ziyang Ma, Xie Chen, Eunsol Choi, David Harwath
arXiv:2607. 24786v1 Announce Type: cross Abstract: Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale.
By Hugo Malard, Michel Olvera, Sanjeel Parekh, Ga\"el Richard, Slim Essid, St\'ephane Lathuili\`ere