# Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition

arXiv: https://arxiv.org/abs/1904.08703 · HTML: https://mlsanity.com/paper/1904.08703/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 30 of 54)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning](https://mlsanity.com/paper/2308.06038/) (2023)
- [Evidential Deep Learning for Open Set Action Recognition](https://mlsanity.com/paper/2107.10161/) (2021)
- [Counterfactual Zero-Shot and Open-Set Visual Recognition](https://mlsanity.com/paper/2103.00887/) (2021)
- [Contrastive Embedding for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2103.16173/) (2021)
- [Elaborative Rehearsal for Zero-shot Action Recognition](https://mlsanity.com/paper/2108.02833/) (2021)
- [Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization](https://mlsanity.com/paper/2302.00624/) (2023)
- [MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge](https://mlsanity.com/paper/2303.08914/) (2023)
- [Discriminative Region-based Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2108.09301/) (2021)
- [Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances](https://mlsanity.com/paper/2409.11884/) (2024)
- [TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition](https://mlsanity.com/paper/2311.08245/) (2023)
- [Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models](https://mlsanity.com/paper/2207.07646/) (2022)
- [Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data](https://mlsanity.com/paper/2310.05010/) (2023)
- [Generative Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2101.11606/) (2021)
- [FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks](https://mlsanity.com/paper/2203.13371/) (2022)
- [Object Priors for Classifying and Localizing Unseen Actions](https://mlsanity.com/paper/2104.04715/) (2021)
- [Cross-modal Representation Learning for Zero-shot Action Recognition](https://mlsanity.com/paper/2205.01657/) (2022)
- [A New Split for Evaluating True Zero-Shot Action Recognition](https://mlsanity.com/paper/2107.13029/) (2021)
- [Synthetic Sample Selection for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2304.02846/) (2023)
- [CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition](https://mlsanity.com/paper/2101.07042/) (2021)
- [Taylor Videos for Action Recognition](https://mlsanity.com/paper/2402.03019/) (2024)
- [Camera Invariant Feature Learning for Generalized Face Anti-spoofing](https://mlsanity.com/paper/2101.10075/) (2021)
- [C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition](https://mlsanity.com/paper/2407.06113/) (2024)
- [Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP](https://mlsanity.com/paper/2412.09895/) (2024)
- [Tell me what you see: A zero-shot action recognition method based on natural language descriptions](https://mlsanity.com/paper/2112.09976/) (2021)
- [An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2406.00639/) (2024)
- [Orthogonal Temporal Interpolation for Zero-Shot Video Recognition](https://mlsanity.com/paper/2308.06897/) (2023)
- [EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning](https://mlsanity.com/paper/2308.09915/) (2023)
- [Text-Enhanced Zero-Shot Action Recognition: A training-free approach](https://mlsanity.com/paper/2408.16412/) (2024)
- [Interaction-Aware Prompting for Zero-Shot Spatio-Temporal Action Detection](https://mlsanity.com/paper/2304.04688/) (2023)

## Abstract (arXiv)

Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categories. As a consequence, unseen category samples are incorrectly classified as belonging to one of the seen action categories. In this paper, we set out to tackle this issue by arguing for a separate treatment of seen and unseen action categories in generalized zero-shot action recognition. We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category. To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos. Experiments are performed on three action recognition datasets: Olympic Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our proposed approach outperforms the baseline (f-CLSWGAN) with absolute gains (in classification accuracy) of 7.0%, 3.4%, and 4.9%, respectively, on these datasets.