A Generative Approach to Zero-Shot and Few-Shot Action Recognition (1801.09086v1)

Abstract: We present a generative framework for zero-shot action recognition where some of the possible action classes do not occur in the training data. Our approach is based on modeling each action class using a probability distribution whose parameters are functions of the attribute vector representing that action class. In particular, we assume that the distribution parameters for any action class in the visual space can be expressed as a linear combination of a set of basis vectors where the combination weights are given by the attributes of the action class. These basis vectors can be learned solely using labeled data from the known (i.e., previously seen) action classes, and can then be used to predict the parameters of the probability distributions of unseen action classes. We consider two settings: (1) Inductive setting, where we use only the labeled examples of the seen action classes to predict the unseen action class parameters; and (2) Transductive setting which further leverages unlabeled data from the unseen action classes. Our framework also naturally extends to few-shot action recognition where a few labeled examples from unseen classes are available. Our experiments on benchmark datasets (UCF101, HMDB51 and Olympic) show significant performance improvements as compared to various baselines, in both standard zero-shot (disjoint seen and unseen classes) and generalized zero-shot learning settings.

• The paper introduces a generative framework that models unseen action classes via attribute-based probabilistic predictions.
• It employs both linear and nonlinear kernel-based regression models to estimate distribution parameters for zero-shot and few-shot recognition.
• Extensive experiments on benchmarks like UCF101 and HMDB51 demonstrate significant performance gains and effective domain adaptation.

A Generative Approach to Zero-Shot and Few-Shot Action Recognition

The paper entitled "A Generative Approach to Zero-Shot and Few-Shot Action Recognition" presents a probabilistic generative framework aimed at addressing the challenges inherent in zero-shot (ZSL) and few-shot learning (FSL) settings within the domain of action recognition. The focus here lies in the ability to classify actions in video data, even when those action categories are unavailable during the model's training phase. Specifically, the paper tackles two scenarios: the inductive setting, wherein only labeled instances of known classes are utilized, and the transductive setting, where additional unlabeled data from unknown classes is available during training.

Methodological Overview

The authors propose a novel framework leveraging a generative approach that models unseen action classes as probability distributions within a visual feature space. More concretely, they assume that the distribution parameters for each action class are functions of attribute vectors, with the parameters being expressible as linear combinations of a learned set of basis vectors. These basis vectors, obtained from labeled examples of seen classes, allow for the prediction of parameters for unseen class distributions.

The paper delineates two primary implementations of this framework: utilizing a linear regression model and a nonlinear kernel-based regression model. Importantly, both methodologies enable the estimation of distribution parameters for unseen classes in closed form. A reverse mapping regularizer is included to enable reconstruction of class attributes from the visual space, akin to an autoencoder mechanism, which minimizes information loss.

Experimentation and Results

Extensive experimentation was conducted using benchmark datasets UCF101, HMDB51, and Olympic to compare the generative approach against various baseline methods. The paper reports promising performance improvements, particularly in the standard zero-shot setting where seen and unseen classes are disjoint, and in the generalized zero-shot setting involving overlapping class sets. Significant performance gains were reported compared to state-of-the-art methods, bolstered by the ability of the generative model to synthesize novel examples, thereby enhancing classifier training in generalized settings.

Practical and Theoretical Implications

Practically, this framework offers a scalable solution to the exhaustive task of annotating extensive datasets in supervised learning scenarios by leveraging inter-class relationships through attribute vectors. Theoretically, the modeling of class distributions as combinations of learned basis vectors opens up new avenues for understanding the transfer of semantic attributes between known and unknown action categories. Crucially, the generative nature of this approach facilitates domain adaptation, utilizing both labeled and unlabeled data to mitigate biases inherent in conventional settings.

Future Directions

The results achieved indicate promising future work, including refining the model's performance through more robust attribute vector representations or exploring alternative distribution models beyond the Gaussian assumption. As machine learning models continue to evolve, integrating such generative frameworks into broader AI systems promises to enhance flexibility and accuracy in diverse application domains, from autonomous driving to automated video surveillance systems.

This work provides a significant contribution to the ongoing development and application of generative approaches in action recognition tasks, underscoring the potential of combining semantic attributes with probabilistic generative models in addressing the challenges inherent in zero- and few-shot learning scenarios.