arXiv:2608.24674v1 Announce Type: new
Abstract: Joint text-to-video-audio generation produces synchronized visual and acoustic content, but the long sampling trajectories and heterogeneous multimodal...
By Xiaoda Yang, Yuxiang Liu, Kaiwen Zheng, Yuan Liu, Yibo Lai, Shengpeng Ji, Kai Jiang, Jianfei Chen, Xiaobin Hu, Shuicheng Yan, Jintao Zhang, Jun Zhu, Zhou Zhao
arXiv:2606. 25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning.
By Revant Teotia, Adrien Bardes, Michael Rabbat, Sumit Chopra, Matthew J. Muckley, Nicolas Ballas
arXiv:2603. 12478v2 Announce Type: replace-cross Abstract: Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven.
By Rujie Wu, Haozhe Zhao, Hai Ci, Yizhou Wang
The paper introduces a novel four‑stream complex‑valued architecture for Open‑Vocabulary Audio‑Visual Event Localization (OV‑AVEL). By embedding visual, audio, and textual modalities into a shared space and computing complex‑valued similarities—real parts from standard embeddings and imaginary parts from companion streams (iHSV for vision and CycleGAN‑translated phase for audio)—the method learns fusion via a complex‑valued neural network while keeping encoders frozen. This approach achieves new state‑of‑the‑art results on OV‑AVEBench and a modified AVE dataset, outperforming previous fine‑tuned baselines by significant margins.
"whyItMatters":"The proposed complex‑valued fusion strategy demonstrates that incorporating phase information and learning fusion in the complex domain can substantially improve open‑vocabulary event localization performance, setting new benchmarks for the task."
By Anirudh Praveen, Koteswar Rao Jerripothula, Pratik Joshi, Aveen Dayal, Neela Sawant
arXiv:2608.31106v1 Announce Type: new
Abstract: Recent video generators often omit audio or synthesize it in a separate stage, limiting reciprocal modeling of visual dynamics and acoustic events. We...
By Jiashu Zhu, Yanhao Zheng, Ruitian Tian, Rujing Dang, Shen Zhang, Bingze Song, Jiachen Lei, Ruimin Lin, Jiahong Wu, Xiangxiang Chu
arXiv:2602. 18528v2 Announce Type: replace Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to poor accuracy.
By Sarthak Kumar Maharana, Akshay Mehra, Bhavya Ramakrishna, Yunhui Guo, Guan-Ming Su