# Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning

arXiv: https://arxiv.org/abs/2503.22197 · HTML: https://mlsanity.com/paper/2503.22197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [Visual Entailment: A Novel Task for Fine-Grained Image Understanding](https://mlsanity.com/paper/1901.06706/) (2019)
- [MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space](https://mlsanity.com/paper/2105.01879/) (2021)
- [Extremely Simple Activation Shaping for Out-of-Distribution Detection](https://mlsanity.com/paper/2209.09858/) (2022)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild](https://mlsanity.com/paper/1605.04253/) (2016)
- [Labelling unlabelled videos from scratch with multi-modal self-supervision](https://mlsanity.com/paper/2006.13662/) (2020)
- [MOOD: Multi-level Out-of-distribution Detection](https://mlsanity.com/paper/2104.14726/) (2021)
- [Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation](https://mlsanity.com/paper/1904.07209/) (2019)
- [Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zeroshot Classification and Retrieval of Videos](https://mlsanity.com/paper/1910.08732/) (2019)
- [Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language](https://mlsanity.com/paper/2203.03598/) (2022)
- [AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings](https://mlsanity.com/paper/2005.13402/) (2020)
- [Temporal and cross-modal attention for audio-visual zero-shot learning](https://mlsanity.com/paper/2207.09966/) (2022)
- [Hyperbolic Audio-visual Zero-shot Learning](https://mlsanity.com/paper/2308.12558/) (2023)
- [Diverse Audio Captioning via Adversarial Training](https://mlsanity.com/paper/2110.06691/) (2021)
- [WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis](https://mlsanity.com/paper/2303.07543/) (2023)
- [Audio-visual Generalized Zero-shot Learning the Easy Way](https://mlsanity.com/paper/2407.13095/) (2024)
- [Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models](https://mlsanity.com/paper/2404.06309/) (2024)
- [Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General Framework](https://mlsanity.com/paper/2408.01284/) (2024)

## Built on by (most cited first, 1 of 1)

- [On Aligning Hierarchical Standardized Embedding for Audio-visual Generalized Zero-shot Learning](https://mlsanity.com/paper/2606.11602/) (2026)

## Abstract (arXiv)

Zero-shot Learning(ZSL) attains knowledge transfer from seen classes to unseen classes by exploring auxiliary category information, which is a promising yet difficult research topic. In this field, Audio-Visual Generalized Zero-Shot Learning~(AV-GZSL) has aroused researchers' great interest in which intricate relations within triple modalities~(audio, video, and natural language) render this task quite challenging but highly research-worthy. However, both existing embedding-based and generative-based AV-GZSL methods tend to suffer from domain shift problem a lot and we propose an extremely simple Out-of-distribution~(OOD) detection based AV-GZSL method~(EZ-AVOOD) to further mitigate bias problem by differentiating seen and unseen samples at the initial beginning. EZ-AVOOD accomplishes effective seen-unseen separation by exploiting the intrinsic discriminative information held in class-specific logits and class-agnostic feature subspace without training an extra OOD detector network. Followed by seen-unseen binary classification, we employ two expert models to classify seen samples and unseen samples separately. Compared to existing state-of-the-art methods, our model achieves superior ZSL and GZSL performances on three audio-visual datasets and becomes the new SOTA, which comprehensively demonstrates the effectiveness of the proposed EZ-AVOOD.