Transductive Zero-Shot Action Recognition by Word-Vector Embedding (1511.04458v2)

Abstract: The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all categories. Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is zero-shot learning" (ZSL). To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training data. Existing ZSL studies focus primarily on still images, and attribute-based semantic representations. In this work, we explore word-vectors as the shared semantic space to embed videos and category labels for ZSL action recognition. This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video spacetime features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross-category domain shift. To solve this generalisation problem in ZSL action recognition, we investigate a series of synergistic strategies to improve upon the standard ZSL pipeline. Most of these strategies are transductive in nature which means access to testing data in the training phase.

Transductive Zero-Shot Action Recognition by Word-Vector Embedding

The paper "Transductive Zero-Shot Action Recognition by Word-Vector Embedding" addresses the problem of action recognition in videos, specifically focusing on the zero-shot learning (ZSL) paradigm. As the number of categories in action recognition grows, traditional recognition models that rely on large sets of labeled training data face scalability challenges. The zero-shot learning approach offers a solution by enabling the recognition of novel categories without requiring labeled visual data for those categories during training. The paper explores the use of word vectors as semantic embedding spaces to facilitate this recognition process.

The authors identified the significant limitations of existing ZSL methods, which predominantly focus on attribute-based representations and still images. In this research, they propose using word-vectors to bridge the gap between video features and category labels, leveraging unsupervised learning techniques (e.g., word-vector representation from large text corpora) to map visual features to the semantic space without relying on predefined attribute ontologies. This semantic space abstraction allows for direct use of category names, thereby simplifying the process of adding new actions to the recognition pipeline.

Given the inherent complexity of mapping video space-time features to semantic descriptors, the authors implement a series of strategies to improve standard zero-shot learning pipelines and mitigate domain-shift challenges — a notorious problem in ZSL due to the disjoint nature of training and testing categories. Their main strategies, which incorporate transductive learning (having access to testing data during training), include:

1. Manifold-Regularized Regression: A technique that leverages semi-supervised learning to incorporate both labeled and unlabeled data in the training process, ensuring smoother regressors that generalize better to unseen classes. This involves constructing a K-nearest neighbor (KNN) graph over the complete set of labeled and testing data, using this as a manifold regularizer in the regression step.
2. Data Augmentation: The process of improving the embedding space by integrating additional datasets, thus providing a richer source of training data and leading to more robust semantics that generalize across various domains. This cross-dataset learning is made seamless by the word-vector embeddings.
3. Transductive Self-Training and Hubness Correction: Post-processing strategies to adapt descriptors at test time, aligning the semantic space with the true structure of the target data, and correcting potential biases introduced by the domain shift problem, such as the 'hubness' phenomenon.

Extensive evaluations performed on diverse human action datasets — HMDB51, UCF101, Olympic Sports — and event datasets, such as CCV and TRECVID MED 13 demonstrated the effectiveness of the proposed framework. The combination of unsupervised semantic embeddings and transductive strategies yielded state-of-the-art performance with efficient closed-form solutions that are computationally simple relative to alternative paradigms requiring supervised annotations.

The findings underline the potential of word-vector embeddings in zero-shot action recognition tasks and highlight the importance of addressing domain-shift issues. The insights provided by the analysis of class affinity and transferability suggest mechanisms for optimizing training set selection in a zero-shot framework. Although challenges remain—particularly in recognizing novel categories amidst known instances—the methodologies employed bring new perspectives to ZSL in action recognition, with overarching implications for enhancing the scalability and adaptability of AI systems in video analysis. Future work may seek to refine these approaches to address deeper complexities within transductive learning contexts and explore broader applications across different domains within autonomous systems and intelligent surveillance.

In summary, this paper contributes meaningfully to the discourse on zero-shot action recognition by systematically exploring and enhancing the robustness of semantic embeddings in handling novel categories efficiently, pioneering paths for future innovations in the AI landscape.