Comput. Sci. Math. Forum, 2024, AIBSD 2024

Standard training via empirical risk minimization may result in making predictions that overly rely on spurious correlations. This can degrade the generalization to out-of-distribution settings where these correlations no longer hold. Invariant learning has been shown to be a promising approach for identifying predictors that ignore spurious correlations. However, an important limitation of this approach is that it assumes access to different “environments” (also known as domains), which may not always be available. This paper proposes a simple yet effective strategy for discovering maximally informative environments from a single dataset. Our frustratingly easy environment discovery (FEED) approach trains a biased reference classifier using a generalized cross-entropy loss function and partitions the dataset based on its performance. These environments can be used with various invariant learning algorithms, including Invariant Risk Minimization, Risk Extrapolation, and Group Distributionally Robust Optimization. The results indicate that FEED can discover environments with a higher group sufficiency gap compared to the state-of-the-art environment inference baseline and leads to improved test accuracy on CMNIST, Waterbirds, and CelebA datasets. Full article

Vision-language models (VLMs) have demonstrated increasing potency in solving complex vision-language tasks in the recent past. Visual question answering (VQA) is one of the primary downstream tasks for assessing the capability of VLMs, as it helps in gauging the multimodal understanding of a VLM in answering open-ended questions. The vast contextual information learned during the pretraining stage in VLMs can be utilised effectively to finetune the VQA model for specific datasets. In particular, special types of VQA datasets, such as OK-VQA, A-OKVQA (outside knowledge-based), and ArtVQA (domain-specific), have a relatively smaller number of images and corresponding question-answer annotations in the training set. Such datasets can be categorised as data-scarce. This hinders the effective learning of VLMs due to the low information availability. We introduce SemIAug (Semi-Supervised Implicit Augmentation), a model and dataset agnostic strategy specially designed to address the challenges faced by limited data availability in the domain-specific VQA datasets. SemIAug uses the annotated image-question data present within the chosen dataset and augments it with meaningful new image-question associations. We show that SemIAug improves the VQA performance on data-scarce datasets without the need for additional data or labels. Full article

Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to a degraded online reinforcement learning performance. In this paper, we propose a model-based data augmentation strategy to maximize the benefits of offline reinforcement learning pre-training and reduce the scale of data needed to be effective. Our approach leverages a world model of the environment trained on the offline dataset to augment states during offline pre-training. We evaluate our approach on a variety of MuJoCo robotic tasks, and our results show that it can jumpstart online fine-tuning and substantially reduce—in some cases by an order of magnitude—the required number of environment interactions. Full article

Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used. Full article

Three-dimensional semantic segmentation is crucial for comprehending transmission line structure and environment. This understanding forms the basis for a variety of applications, such as automatic risk assessment of line tripping caused by wildfires, wind, and thunder. However, the performance of current 3D point cloud segmentation methods tends to degrade on imbalanced data, which negatively impacts the overall segmentation results. In this paper, we proposed an imBalanced-Aware Long-Range 3D Semantic Segmentation framework (iBALR3D) which is specifically designed for large-scale transmission line segmentation. To address the unsatisfactory performance on categories with few points, an Enhanced Imbalanced Contrastive Learning module is first proposed to improve feature discrimination between points across sampling regions by contrasting the representations with the assistance of data augmentation. A structural Adaptive Spatial Encoder is designed to capture the distinguish measures across different components. Additionally, we employ a sampling strategy to enable the model to concentrate more on regions of categories with few points. This strategy further enhances the model’s robustness in handling challenges associated with long-range and significant data imbalances. Finally, we introduce a large-scale 3D point cloud dataset (500KV3D) captured from high-voltage long-range transmission lines and evaluate iBALR3D on it. Extensive experiments demonstrate the effectiveness and superiority of our approach. Full article