PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing (1805.04409v1)

Abstract: Depth estimation and scene parsing are two particularly important tasks in visual scene understanding. In this paper we tackle the problem of simultaneous depth estimation and scene parsing in a joint CNN. The task can be typically treated as a deep multi-task learning problem [42]. Different from previous methods directly optimizing multiple tasks given the input training data, this paper proposes a novel multi-task guided prediction-and-distillation network (PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging from low level to high level, and then the predictions from these intermediate auxiliary tasks are utilized as multi-modal input via our proposed multi-modal distillation modules for the final tasks. During the joint learning, the intermediate tasks not only act as supervision for learning more robust deep representations but also provide rich multi-modal information for improving the final tasks. Extensive experiments are conducted on two challenging datasets (i.e. NYUD-v2 and Cityscapes) for both the depth estimation and scene parsing tasks, demonstrating the effectiveness of the proposed approach.

• The paper presents PAD-Net, a multi-task framework that guides depth estimation and scene parsing using intermediate task predictions.
• It innovatively employs auxiliary tasks such as monocular depth prediction, surface normal estimation, contour detection, and semantic parsing to enhance feature learning.
• Extensive experiments on NYUD-v2 and Cityscapes confirm significant improvements over standard metrics, underscoring its potential for real-world applications.

Overview of PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing

The paper "PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing" introduces a novel approach to visual scene understanding using a multi-task prediction-and-distillation network, termed PAD-Net. The proposed method innovatively addresses depth estimation and scene parsing—two crucial tasks in visual perception—by leveraging the synergy between them in a joint CNN framework.

Summary of Techniques

The PAD-Net architecture pioneers a two-phase process. Initially, it predicts a set of intermediate auxiliary tasks, which include gradual tasks from low-level to high-level, such as monocular depth prediction, surface normal estimation, contour prediction, and semantic parsing. These tasks serve dual purposes: they guide the network towards more robust representation learning and provide extensive multi-modal information for optimizing the ultimate output tasks.

This methodology distinguishes itself from traditional paradigms, which typically focus on final-task predictions without considering intermediate task outputs as multi-modal inputs for improving final objectives. The PAD-Net instead uses these predictions as guidance through multi-modal distillation modules to refine depth estimation and scene parsing tasks.

Experimental Validation

By conducting extensive experiments on the NYUD-v2 and Cityscapes datasets, the paper demonstrates significant gains in both tasks. Key improvements were observed on standard metrics used in the field, including mean IoU for parsing, relative error, and RMSE for depth estimation. On NYUD-v2, PAD-Net achieves state-of-the-art results for both tasks, evidencing the robustness and efficacy of the proposed approach.

Implications

The practical implications of PAD-Net are manifold. It offers a compelling solution for applications requiring precise scene understanding, such as autonomous vehicles and robotics, due to its enhanced depth and segmentation performance. Theoretically, PAD-Net contributes toward enriching our understanding of how intermediate supervision and task interdependencies can be harnessed in deep learning models. The multi-modal distillation mechanism is particularly noteworthy, potentially inspiring further innovations in multi-task learning frameworks that might be generalized to other domains.

Future Prospects

Looking forward, the work lays fertile ground for various avenues of exploration in AI. One such trajectory could involve extending PAD-Net to accommodate additional perceptual tasks or modalities, thus creating a more comprehensive framework for scene understanding. Furthermore, exploring the integration of PAD-Net within larger systems—such as those used in real-time applications—could examine its adaptability and efficiency under operational constraints.

Ultimately, the PAD-Net model stands as a significant contribution to multi-task learning by demonstrating how intermediate prediction tasks can be strategically leveraged to bolster performance in complex visual tasks. Through its innovative architecture and promising results, it underscores the potential of joint task optimization to meet the escalating demands of advanced AI applications.