# Global Semantic Descriptors for Zero-Shot Action Recognition

arXiv: https://arxiv.org/abs/2209.12061 · HTML: https://mlsanity.com/paper/2209.12061/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 24 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [A Short Note on the Kinetics-700 Human Action Dataset](https://mlsanity.com/paper/1907.06987/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Knowledge distillation: A good teacher is patient and consistent](https://mlsanity.com/paper/2106.05237/) (2021)
- [Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications](https://mlsanity.com/paper/2003.01455/) (2020)
- [Elaborative Rehearsal for Zero-shot Action Recognition](https://mlsanity.com/paper/2108.02833/) (2021)
- [Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition](https://mlsanity.com/paper/1904.08703/) (2019)
- [Towards Universal Representation for Unseen Action Recognition](https://mlsanity.com/paper/1803.08460/) (2018)
- [A Generative Approach to Zero-Shot and Few-Shot Action Recognition](https://mlsanity.com/paper/1801.09086/) (2018)
- [Zero-Shot Visual Recognition via Bidirectional Latent Embedding](https://mlsanity.com/paper/1607.02104/) (2016)
- [Objects2action: Classifying and localizing actions without any video example](https://mlsanity.com/paper/1510.06939/) (2015)
- [Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions](https://mlsanity.com/paper/1707.09145/) (2017)
- [Zero-Shot Action Recognition in Videos: A Survey](https://mlsanity.com/paper/1909.06423/) (2019)
- [Object Priors for Classifying and Localizing Unseen Actions](https://mlsanity.com/paper/2104.04715/) (2021)
- [A New Split for Evaluating True Zero-Shot Action Recognition](https://mlsanity.com/paper/2107.13029/) (2021)
- [CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition](https://mlsanity.com/paper/2101.07042/) (2021)
- [Zero-Shot Action Recognition from Diverse Object-Scene Compositions](https://mlsanity.com/paper/2110.13479/) (2021)
- [Tell me what you see: A zero-shot action recognition method based on natural language descriptions](https://mlsanity.com/paper/2112.09976/) (2021)
- [Dense Video Captioning Using Unsupervised Semantic Information](https://mlsanity.com/paper/2112.08455/) (2021)
- [Universal Prototype Transport for Zero-Shot Action Recognition and Localization](https://mlsanity.com/paper/2203.03971/) (2022)

## Built on by (most cited first, 2 of 2)

- [REST: REtrieve & Self-Train for generative action recognition](https://mlsanity.com/paper/2209.15000/) (2022)
- [CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition](https://mlsanity.com/paper/2605.01165/) (2026)

## Abstract (arXiv)

The success of Zero-shot Action Recognition (ZSAR) methods is intrinsically related to the nature of semantic side information used to transfer knowledge, although this aspect has not been primarily investigated in the literature. This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences. We demonstrate that representing all object classes using descriptive sentences generates an accurate object-action affinity estimation when a paraphrase estimation method is used as an embedder. We also show how to estimate probabilities over the set of action classes based only on a set of sentences without hard human labeling. In our method, the probabilities from these two global classifiers (i.e., which use features computed over the entire video) are combined, producing an efficient transfer knowledge model for action classification. Our results are state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101 under the ZSAR evaluation. Our code is available at https://github.com/valterlej/objsentzsar