arXiv:2607. 01297v1 Announce Type: cross Abstract: Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes).
By Yanxiong Li, Jiaxin Tan, Qianqian Li, Guoqing Chen, Sen Huang, Tuomas Virtanen
arXiv:2605. 13672v1 Announce Type: cross Abstract: Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are independent of contextual cues.
By Giries Abu Ayoub, Morad Tukan, Loay Mualem
Fully few-shot class-incremental audio classification (FFCAC) requires recognizing new sound classes from only a handful of labeled examples per session, without forgetting previously learned classes...
arXiv:2606. 08898v1 Announce Type: cross Abstract: In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease.
By Yanxiong Li, Guoqing Chen, Qianqian Li, Sen Huang
arXiv:2609.17076v1 Announce Type: new
Abstract: Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting represe...
By Fengrui Liu, Ningxin Shen, Yi Li, Yiwei Fu, Feng Liu, Jiangmeng Li
The paper introduces Spectral Transductive Refinement (STR), a training‑free method that refines class prototypes at test time using the geometry of a joint k‑nearest‑neighbour graph and a normalized‑Laplacian spectral coordinate system. STR operates solely on frozen visual embeddings, iteratively updating pseudo‑labelled queries to improve one‑shot and few‑shot classification under domain shift. Experiments on ResNet‑18 and ResNet‑10 backbones show STR outperforms single‑prototype baselines and rivals meta‑trained cross‑domain few‑shot methods, achieving the best 1‑shot average across eight target domains.
By Fahim Rahman, S. M. Tanjeeb Meheran Rohan, Md. Taimum Ibne Sayed, Asaduzzaman Herok, Md. Bakhtiar Hasan