Auxiliary Tasks in Multi-task Learning (1805.06334v2)

Abstract: Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expected to boost the performance of the ultimately desired main tasks. To study the proposed approach, we picked vision-based road scene understanding (RSU) as an exemplary application. Since multi-task learning requires specialized datasets, particularly when using extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU, called synMT. More than 2.5 $\cdot$ 10^5 synthetic images, annotated with 21 different labels, were acquired from the video game Grand Theft Auto V (GTA V). Our proposed deep multi-task CNN architecture was trained on various combination of tasks using synMT. The experiments confirmed that auxiliary tasks can indeed boost network performance, both in terms of final results and training time.

• The paper presents a novel strategy that integrates auxiliary tasks to regularize and enhance the performance of main tasks in multi-task learning.
• It utilizes a deep CNN with a ResNet50 backbone and task-specific decoder branches, validated on a synthetic GTA V street scene dataset.
• Results indicate improved depth prediction accuracy and faster training convergence, underscoring benefits for autonomous vehicle applications.

An Analysis of Auxiliary Tasks in Multi-task Learning for Road Scene Understanding

The paper "Auxiliary Tasks in Multi-task Learning" by Lukas Liebel and Marco Körner examines a novel strategy for enhancing multi-task learning (MTL) in the domain of computer vision (CV), particularly focusing on road scene understanding (RSU). This research investigates the efficacy of incorporating auxiliary tasks into MTL frameworks to improve performance on main tasks that are critical for applications such as autonomous vehicles (AV) and advanced driver assistance systems (ADAS).

Motivation and Methodology

Multi-task learning aims to simultaneously address multiple tasks by discovering a shared structure in data representations, which can lead to improved generalization compared to single-task learning. The authors propose introducing auxiliary tasks, defined as ancillary or supportive tasks that might not be directly relevant to the main tasks but can influence the learning of robust and generalized features. The intention is that these auxiliary tasks serve as a form of regularization, preventing overfitting and potentially accelerating convergence during training.

The paper focuses on vision-based RSU, exemplifying the typical components needed for AV systems such as lane detection and obstacle recognition. To this end, the authors curated a large-scale synthetic dataset, "synMT," extracted from the video game Grand Theft Auto V (GTA V), comprising over 250,000 images with 21 different labels. This meticulously annotated dataset facilitated the training and evaluation of their proposed deep multi-task convolutional neural network (CNN) architecture. The authors utilized established CV techniques like single-image depth estimation (SIDE) and semantic segmentation (semseg) as main tasks, while treating time of day and weather conditions as auxiliary tasks.

Experimental Approach

The authors implemented an encoder-decoder CNN architecture influenced by the DeepLabv3 model, using a ResNet50 backbone extended with task-specific decoder branches for each task. This architecture allows for the simultaneous learning of multiple tasks while yielding separate outputs for each. The paper conducted several experiments comparing various combinations of tasks to observe performance variations. Concretely, setups without auxiliary tasks were contrasted with those incorporating one or both auxiliary tasks. The performance metrics utilized include mean intersection over union (MIoU) for semseg, root mean squared error (RMSE) for depth estimation, root mean squared cyclic time difference (RMSCTD) for time estimation, and classification accuracy for weather prediction.

Results and Insights

The experimental results demonstrate that the inclusion of auxiliary tasks can indeed bolster the efficacy of main tasks. Notably, leveraging auxiliary tasks such as weather classification enhanced the depth prediction capabilities (yielding an RMSE of 0.05704 for the scenario including auxiliary tasks). Moreover, some auxiliary task configurations accelerated convergence during training, illustrating their regularizing effect by reducing variance in model updates.

The paper reveals that the choice of auxiliary tasks must be carefully considered to maximize their beneficial impact on main tasks. Furthermore, the synergy between seemingly unrelated tasks in MTL suggests potential for designing more robust AV systems using similar strategies. The use of synthetic datasets like synMT played a crucial role in augmenting the dataset diversity affordably, underscoring the value of synthetic data generation for rare or dangerous real-world scenarios.

Implications and Future Directions

This research offers foundational insights on utilizing auxiliary tasks to fortify MTL frameworks. Practically, it indicates potential strategies for improving the training efficiency and accuracy of AV perception systems, suggesting auxiliary task inclusion could be a fruitful direction for future inquiries.

Theoretically, it opens pathways for exploring the dynamics between main and auxiliary tasks, which could lead to improved customization of learning models based on specific domain requirements. In future work, elucidating methods for systematically defining auxiliary tasks and quantitatively assessing their overlap and contribution to main tasks could optimize model design further.

Overall, Liebel and Körner's investigation advances the understanding of MTL's capabilities in RSU, establishing promising avenues for academic and applied research in CV and AI more broadly.