“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.

/pdf/dynamic-graph-cnn-for-learning-on-point-clouds-20wlcru9tg.pdf

Dynamic Graph CNN for Learning on Point Clouds

On robust estimation of the location parameter

The PASCAL Visual Object Classes Challenge

One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

/pdf/spectral-normalization-for-generative-adversarial-networks-3dxutlj1bi.pdf

Spectral Normalization for Generative Adversarial Networks

The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffering from its complex structure and error propagation problem. Approaches have been proposed to reduce the complexity by replacing motion prediction modules with implicit flow networks. Error propagation aware training strategy is also proposed to alleviate incremental reconstruction errors from previously decoded frames. Although these methods have brought some improvement, little attention has been paid to the framework itself. Inspired by the success of learned image compression through simplifying the framework with a single deep neural network, it is natural to expect a better performance in video compression via a simple yet appropriate framework. Therefore, we propose a framework to directly compress raw-pixel frames (rather than residual images), where no extra motion prediction module is required. Instead, an entropy model is used to estimate the spatiotemporal redundancy in a latent space rather than pixel level, which significantly reduces the complexity of the framework. Specifically, the whole framework is a compression module, consisting of a unified auto-encoder which produces identically distributed latents for all frames, and a spatiotemporal entropy estimation model to minimize the entropy of these latents. Experiments showed that the proposed method outperforms state-of-the-art (SOTA) performance under the metric of multiscale structural similarity (MS-SSIM) and achieves competitive results under the metric of PSNR.

Spatiotemporal Entropy Model is All You Need for Learned Video Compression.

High-fidelity face digitization solutions often combine multi-view stereo
(MVS) techniques for 3D reconstruction and a non-rigid registration step to
establish dense correspondence across identities and expressions. A common
problem is the need for manual clean-up after the MVS step, as 3D scans are
typically affected by noise and outliers and contain hairy surface regions that
need to be cleaned up by artists. Furthermore, mesh registration tends to fail
for extreme facial expressions. Most learning-based methods use an underlying
3D morphable model (3DMM) to ensure robustness, but this limits the output
accuracy for extreme facial expressions. In addition, the global bottleneck of
regression architectures cannot produce meshes that tightly fit the ground
truth surfaces. We propose ToFu, Topologically consistent Face from multi-view,
a geometry inference framework that can produce topologically consistent meshes
across facial identities and expressions using a volumetric representation
instead of an explicit underlying 3DMM. Our novel progressive mesh generation
network embeds the topological structure of the face in a feature volume,
sampled from geometry-aware local features. A coarse-to-fine architecture
facilitates dense and accurate facial mesh predictions in a consistent mesh
topology. ToFu further captures displacement maps for pore-level geometric
details and facilitates high-quality rendering in the form of albedo and
specular reflectance maps. These high-quality assets are readily usable by
production studios for avatar creation, animation and physically-based skin
rendering. We demonstrate state-of-the-art geometric and correspondence
accuracy, while only taking 0.385 seconds to compute a mesh with 10K vertices,
which is three orders of magnitude faster than traditional techniques. The code
and the model are available for research purposes at
https://tianyeli.github.io/tofu.

Topologically Consistent Multi-View Face Inference Using Volumetric Sampling

Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in each updating step, and good performance. Standard excess risk bounds show that SGD only needs to take one pass over the training data and more passes could not help to improve the performance. Empirically, it has been observed that SGD taking more than one pass over the training data (multi-pass SGD) has much better excess risk bound performance than the SGD only taking one pass over the training data (one-pass SGD). However, it is not very clear that how to explain this phenomenon in theory. In this paper, we provide some theoretical evidences for explaining why multiple passes over the training data can help improve performance under certain circumstance. Specifically, we consider smooth risk minimization problems whose objective function is non-convex least squared loss. Under Polyak-Lojasiewicz (PL) condition, we establish faster convergence rate of excess risk bound for multi-pass SGD than that for one-pass SGD.

Why Does Multi-Epoch Training Help?

Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies. However, higher-order equivariant features often come with an exponentially-growing computational cost. Furthermore, it remains relatively less explored how rotation-equivariant features can be leveraged to tackle 3D shape alignment tasks. While many past approaches have been based on either non-equivariant or invariant descriptors to align 3D shapes, we argue that such tasks may benefit greatly from an equivariant framework. In this paper, we propose an effective and practical SE(3) (3D translation and rotation) equivariant network for point cloud analysis that addresses both problems. First, we present SE(3) separable point convolution, a novel framework that breaks down the 6D convolution into two separable convolutional operators alternatively performed in the 3D Euclidean and SO(3) spaces. This significantly reduces the computational cost without compromising the performance. Second, we introduce an attention layer to effectively harness the expressiveness of the equivariant features. While jointly trained with the network, the attention layer implicitly derives the intrinsic local frame in the feature space and generates attention vectors that can be integrated into different alignment tasks. We evaluate our approach through extensive studies and visual interpretations. The empirical results demonstrate that our proposed model outperforms strong baselines in a variety of benchmarks

Equivariant Point Network for 3D Point Cloud Analysis

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most of existing algorithms on benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, those patterns are often learned from super-class labels due to a limited availability of fine-grained labels, while fine-grained patterns are desired in many real-world applications such as visual search in online shopping. To mitigate the challenge, we propose an algorithm to learn the fine-grained patterns sufficiently when only super-class labels are available. The effectiveness of our method can be guaranteed with the theoretical analysis. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance on target tasks corresponding to fine-grained classes, when only super-class information is available for training.

Hao Li

Papers

Spatiotemporal Entropy Model is All You Need for Learned Video Compression.

Topologically Consistent Multi-View Face Inference Using Volumetric Sampling

Why Does Multi-Epoch Training Help?

Equivariant Point Network for 3D Point Cloud Analysis

Representation Learning with Fine-grained Patterns.