Few-shot video action recognition is an effective approach to recognizing new categories with only a few labeled examples, thereby reducing the challenges associated with collecting and annotating large-scale video datasets. Existing methods in video action recognition rely on large labeled datasets from the same domain. However, this setup is not realistic as novel categories may come from different data domains that may have different spatial and temporal characteristics. This dissimilarity between the source and target domains can pose a significant challenge, rendering traditional few-shot action recognition techniques ineffective. To address this issue, in this work, we propose a novel cross-domain few-shot video action recognition method that leverages self-supervised learning and curriculum learning to balance the information from the source and target domains. To be particular, our method employs a masked autoencoder-based self-supervised training objective to learn from both source and target data in a self-supervised manner. Then a progressive curriculum balances learning the discriminative information from the source dataset with the generic information learned from the target domain. Initially, our curriculum utilizes supervised learning to learn class discriminative features from the source data. As the training progresses, we transition to learning target-domain-specific features. We propose a progressive curriculum to encourage the emergence of rich features in the target domain based on class discriminative supervised features in the source domain. We evaluate our method on several challenging benchmark datasets and demonstrate that our approach outperforms existing cross-domain few-shot learning techniques. Our code is available at https://github.com/Sarinda251/CDFSL-V

Adversarial attacks perturb data to modify a model’s prediction. These perturbations can be crafted in a white-box or black-box setting, depending on whether the target model architecture/weights are known or unknown. Compromised architecture and weights of a model makes it vulnerable to the more powerful white-box attacks. In this work, we determine if a deep model is compromised by distinguishing white-box from black-box adversarial attacks. The proposed method utilizes the internal representations of the target model and a proxy model to increase the detector efficacy. Additionally, it employs a spatial smoothing module to control the strength of white-box attacks relative to black-box attacks, and a proxy module to aid in measuring the transferability of the attack. Both modules work in tandem to increase the contrast of the internal representations between white-box and black-box attacks for better discrimination. We perform a detailed ablation of our method to showcase the importance of the different modules, and show that the spatial smoothing and proxy defense techniques enable our framework to significantly outperform the simple classification baseline on common vision datasets.

Deep neural networks are behind many of the recent successes in machine learning applications. However, these models can produce overconfident decisions while encountering out-of-distribution (OOD) examples or making a wrong prediction. This inconsistent predictive confidence limits the integration of independently-trained learning models into a larger system. This paper introduces separable concept learning framework to realistically measure the performance of classifiers in presence of OOD examples. In this setup, several instances of a classifier are trained on different parts of a partition of the set of classes. Later, the performance of the combination of these models is evaluated on a separate test set. Unlike current OOD detection techniques, this framework does not require auxiliary OOD datasets and does not separate classification from detection performance. Furthermore, we present a new strong baseline for more consistent predictive confidence in deep models, called fitted ensembles, where overconfident predictions are rectified by transformed versions of the original classification task. Fitted ensembles can naturally detect OOD examples without requiring auxiliary data by observing contradicting predictions among its components. Experiments on MNIST, SVHN, CIFAR-10/100, and ImageNet show fitted ensemble significantly outperform conventional ensembles on OOD examples and are possible to scale.

Although the introduction of deep learning has led to significant performance improvements in many machine learning applications, several recent studies have revealed that deep feedforward models are easily fooled. Fooling in effect results from overgeneralization of neural networks over regions far from the training data. To circumvent this problem this paper proposes a novel elaboration of standard neural network architectures called the competitive overcomplete output layer (COOL) neural network. Experiments demonstrate the effectiveness of COOL by visualizing the behavior of COOL networks in a low-dimensional artificial classification problem and by applying it to a high-dimensional vision domain (MNIST).