AudioFM is a self‑supervised framework that learns audio representations by combining recording‑level contrastive learning with patch‑wise masked latent discrimination. The model uses visible acoustic context for a student to predict masked patch representations and matches them to full‑view teacher targets from other recordings, while also incorporating weaker supervision from temporal neighbors to promote local consistency. Evaluations show that AudioFM achieves state‑of‑the‑art results across diverse audio domains in linear probing, fine‑tuning, and zero‑shot transfer, excelling in event detection, scene‑level understanding, and domain generalizability.
By Yamin Li, Ruidong Zhang, Anran Wang, Eduardo Fonseca, Dan Ellis, Zechen Li, Simon A. Lee, Tao Pang, Jake Garrison, Maxwell A. Xu, Yicheng Wang, Jitian Zhang, Weizhi Zhang, Haichao Zhang, Catie Chang, Xin Liu, Anupam Pathak, Daniel McDuff, Shwetak Patel, Jake Sunshine, Yuzhe Yang, Xiaoran Fan
Learning audio representations that effectively bridge holistic global soundscapes with fine-grained acoustic moments is a long-standing goal in self-supervised learning. Recording-level contrastive o...
arXiv:2609.06460v1 Announce Type: new
Abstract: Recently, Masked Pretraining (MPT) based on reconstruction pretraining tasks has risen to a promising self-supervised learning paradigm across various...
By Qi Zhang, Runyu Zhou, Yifei Wang, Yisen Wang
arXiv:2608.23234v1 Announce Type: new
Abstract: In this technical report, we present a training-free framework for audio-guided video object segmentation, which integrates Multimodal Large Language M...
By Liangtao Shi, Jinxia Xie, Xiantao Hu, Ting Liu
arXiv:2609.40347v1 Announce Type: new
Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of r...
By Owais Iqbal, Sudipta Sarkar, Shyam Marjit, Omprakash Chakraborty, Anirban Chakraborty, Abir Das
arXiv:2609.36503v1 Announce Type: new
Abstract: Adding or removing a sounding object requires coordinated changes to visual content and sound while preserving the surrounding scene. Yet paired superv...
By Weihan Xu, Kan Jen Cheng, Koichi Saito, Jingyu Shi, Tingle Li, Yisi Liu, Liming Wang, Masato Ishii, Takashi Shibuya, Gopala Anumanchipalli, Paul Pu Liang