arXiv AI June 4, 2026 By Giries Abu Ayoub, Morad Tukan, Loay Mualem

SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

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.

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arXiv AI

Sep 16

Sample-Conditioned Representation Selection for Audio Few-Shot Learning

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

arXiv Machine Learning

Jul 3

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

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

rag

arXiv Computer Vision

Sep 1

Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation

arXiv:2608.29121v1 Announce Type: new Abstract: Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of cat...

By Tianrui Hui, Shaofei Huang, Qisong Han, Yaxiong Wang, Lechao Cheng, Zhedong Zheng, Zhun Zhong, Richang Hong, Meng Wang

computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 16

Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models

arXiv:2606. 15751v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text.

By Hyebin Cho, Jaehyuk Jang, Changick Kim, Joon Son Chung

llms

Hugging Face Trending Papers

Jun 24

From Sounds to Scenes: A Benchmark for Evaluating Context-Aware Auditory Scene Understanding in Large Audio Language Models

Recent Large Audio Language Models (LALMs) have achieved remarkable progress in audio perceptual tasks across individual acoustic layers, including speech, sound, and music. However, existing benchmarks predominantly evaluate these layers in isolation, overlooking the complex contextual relationships that arise when multiple acoustic sources co-occur in real-world auditory scenes.

llms nlp benchmarks

Hugging Face Trending Papers

Sep 15

Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?

Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluations and inefficient c...

llms multimodal benchmarks