Does Monocular Depth Estimation Provide Better Pre-training than Classification for Semantic Segmentation?

Training a deep neural network for semantic segmentation is labor-intensive, so it is common to pre-train it for a different task, and then fine-tune it with a small annotated dataset. State-of-the-art methods use image classification for pre-training, which introduces uncontrolled biases. We test the hypothesis that depth estimation from unlabeled videos may provide better pre-training. Despite the absence of any semantic information, we argue that estimating scene geometry is closer to the task of semantic segmentation than classifying whole images into semantic classes. Since analytical validation is intractable, we test the hypothesis empirically by introducing a pre-training scheme that yields an improvement of 5.7% mIoU and 4.1% pixel accuracy over classification-based pre-training. While annotation is not needed for pre-training, it is needed for testing the hypothesis. We use the KITTI (outdoor) and NYU-V2 (indoor) benchmarks to that end, and provide an extensive discussion of the benefits and limitations of the proposed scheme in relation to existing unsupervised, self-supervised, and semi-supervised pre-training protocols

Optical Flow Estimation in the Deep Learning Age

Akin to many subareas of computer vision, the recent advances in deep learning have also significantly influenced the literature on optical flow. Previously, the literature had been dominated by classical energy-based models, which formulate optical flow estimation as an energy minimization problem. However, as the practical benefits of Convolutional Neural Networks (CNNs) over conventional methods have become apparent in numerous areas of computer vision and beyond, they have also seen increased adoption in the context of motion estimation to the point where the current state of the art in terms of accuracy is set by CNN approaches. We first review this transition as well as the developments from early work to the current state of CNNs for optical flow estimation. Alongside, we discuss some of their technical details and compare them to recapitulate which technical contribution led to the most significant accuracy improvements. Then we provide an overview of the various optical flow approaches introduced in the deep learning age, including those based on alternative learning paradigms (e.g., unsupervised and semi-supervised methods) as well as the extension to the multi-frame case, which is able to yield further accuracy improvements.Comment: To appear as a book chapter in Modelling Human Motion, N. Noceti, A. Sciutti and F. Rea, Eds., Springer, 202

Deep generative models for solving geophysical inverse problems

My thesis presents several novel methods to facilitate solving large-scale inverse problems by utilizing recent advances in machine learning, and particularly deep generative modeling. Inverse problems involve reliably estimating unknown parameters of a physical model from indirect observed data that are noisy. Solving inverse problems presents primarily two challenges. The first challenge is to capture and incorporate prior knowledge into ill-posed inverse problems whose solutions cannot be uniquely identified. The second challenge is the computational complexity of solving inverse problems, particularly the cost of quantifying uncertainty. The main goal of this thesis is to address these issues by developing practical data-driven methods that are scalable to geophysical applications in which access to high-quality training data is often limited. There are six papers included in this thesis. A majority of these papers focus on addressing computational challenges associated with Bayesian inference and uncertainty quantification, while others focus on developing regularization techniques to improve inverse problem solution quality and accelerate the solution process. These papers demonstrate the applicability of the proposed methods to seismic imaging, a large-scale geophysical inverse problem with a computationally expensive forward operator for which sufficiently capturing the variability in the Earth's heterogeneous subsurface through a training dataset is challenging. The first two papers present computationally feasible methods of applying a class of methods commonly referred to as deep priors to seismic imaging and uncertainty quantification. I also present a systematic Bayesian approach to translate uncertainty in seismic imaging to uncertainty in downstream tasks performed on the image. The next two papers aim to address the reliability concerns surrounding data-driven methods for solving Bayesian inverse problems by leveraging variational inference formulations that offer the benefits of fully-learned posteriors while being directly informed by physics and data. The last two papers are concerned with correcting forward modeling errors where the first proposes an adversarially learned postprocessing step to attenuate numerical dispersion artifacts in wave-equation simulations due to coarse finite-difference discretizations, while the second trains a Fourier neural operator surrogate forward model in order to accelerate the qualification of uncertainty due to errors in the forward model parameterization.Ph.D

Deep 3D Information Prediction and Understanding

3D information prediction and understanding play significant roles in 3D visual perception. For 3D information prediction, recent studies have demonstrated the superiority of deep neural networks. Despite the great success of deep learning, there are still many challenging issues to be solved. One crucial issue is how to learn the deep model in an unsupervised learning framework. In this thesis, we take monocular depth estimation as an example to study this problem through exploring the domain adaptation technique. Apart from the prediction from a single image or multiple images, we can also estimate the depth from multi-modal data, such as RGB image data coupled with 3D laser scan data. Since the 3D data is usually sparse and irregularly distributed, we are required to model the contextual information from the sparse data and fuse the multi-modal features. We examine the issues by studying the depth completion task. For 3D information understanding, such as point clouds analysis, due to the sparsity and unordered property of 3D point cloud, instead of the conventional convolution, new operations which can model the local geometric shape are required. We design a basic operation for point cloud analysis through introducing a novel adaptive edge-to-edge interaction learning module. Besides, due to the diversity in configurations of the 3D laser scanners, the captured 3D data often varies from dataset to dataset in object size, density, and viewpoints. As a result, the domain generalization in 3D data analysis is also a critical problem. We study this issue in 3D shape classification by proposing an entropy regularization term. Through studying four specific tasks, this thesis focuses on several crucial issues in deep 3D information prediction and understanding, including model designing, multi-modal fusion, sparse data analysis, unsupervised learning, domain adaptation, and domain generalization

Learning to Enhance RGB and Depth Images with Guidance

Image enhancement improves the visual quality of the input image to better identify key features and make it more suitable for other vision applications. Structure degradation remains a challenging problem in image enhancement, which refers to blurry edges or discontinuous structures due to unbalanced or inconsistent intensity transitions on structural regions. To overcome this issue, it is popular to make use of a guidance image to provide additional structural cues. In this thesis, we focus on two image enhancement tasks, i.e., RGB image smoothing and depth image completion. Through the two research problems, we aim to have a better understanding of what constitutes suitable guidance and how its proper use can benefit the reduction of structure degradation in image enhancement. Image smoothing retains salient structures and removes insignificant textures in an image. Structure degradation results from the difficulty in distinguishing structures and textures with low-level cues. Structures may be inevitably blurred if the filter tries to remove some strong textures that have high contrast. Moreover, these strong textures may also be mistakenly retained as structures. We address this issue by applying two forms of guidance for structures and textures respectively. We first design a kernel-based double-guided filter (DGF), where we adopt semantic edge detection as structure guidance, and texture decomposition as texture guidance. The DGF is the first kernel filter that simultaneously leverages structure guidance and texture guidance to be both ''structure-aware'' and ''texture-aware''. Considering that textures present high randomness and variations in spatial distribution and intensities, it is not robust to localize and identify textures with hand-crafted features. Hence, we take advantage of deep learning for richer feature extraction and better generalization. Specifically, we generate synthetic data by blending natural textures with clean structure-only images. With the data, we build a texture prediction network (TPN) that estimates the location and magnitude of textures. We then combine the texture prediction results from TPN with a semantic structure prediction network so that the final texture and structure aware filtering network (TSAFN) is able to distinguish structures and textures more effectively. Our model achieves superior smoothing results than existing filters. Depth completion recovers dense depth from sparse measurements, e.g., LiDAR. Existing depth-only methods use sparse depth as the only input and suffer from structure degradation, i.e., failing to recover semantically consistent boundaries or small/thin objects due to (1) the sparse nature of depth points and (2) the lack of images to provide structural cues. In the thesis, we deal with the structure degradation issue by using RGB image guidance in both supervised and unsupervised depth-only settings. For the supervised model, the unique design is that it simultaneously outputs a reconstructed image and a dense depth map. Specifically, we treat image reconstruction from sparse depth as an auxiliary task during training that is supervised by the image. For the unsupervised model, we regard dense depth as a reconstructed result of the sparse input, and formulate our model as an auto-encoder. To reduce structure degradation, we employ the image to guide latent features by penalizing their difference in the training process. The image guidance loss in both models enables them to acquire more dense and structural cues that are beneficial for producing more accurate and consistent depth values. For inference, the two models only take sparse depth as input and no image is required. On the KITTI Depth Completion Benchmark, we validate the effectiveness of the proposed image guidance through extensive experiments and achieve competitive performance over state-of-the-art supervised and unsupervised methods. Our approach is also applicable to indoor scenes