OpenRaiser/Intern-Atlas huggingface dataset:
Face sketch to photo translation using generative adversarial networks
Venue: arXiv cs.CV 2021
Year: 2021

Translating face sketches to photo-realistic faces is an interesting and essential task in many applications like law enforcement and the digital entertainment industry. One of the most important challenges of this task is the inherent differences between the sketch and the real image such as the lack of color and details of the skin tissue in the sketch. With the advent of adversarial generative models, an increasing number of methods have been proposed for sketch-to-image synthesis. However, these models still suffer from limitations such as the large number of paired data required for training, the low resolution of the produced images, or the unrealistic appearance of the generated images. In this paper, we propose a method for converting an input facial sketch to a colorful photo without the need for any paired dataset. To do so, we use a pre-trained face photo generating model to synthesize high-quality natural face photos and employ an optimization procedure to keep high-fidelity to the input sketch. We train a network to map the facial features extracted from the input sketch to a vector in the latent space of the face generating model. Also, we study different optimization criteria and compare the results of the proposed model with those of the state-of-the-art models quantitatively and qualitatively. The proposed model achieved 0.655 in the SSIM index and 97.59% rank-1 face recognition rate with higher quality of the produced images.
Conditional Generation of Periodic Signals with Fourier-Based Decoder
Venue: arXiv cs.NE 2021
Year: 2021

Periodic signals play an important role in daily lives. Although conventional sequential models have shown remarkable success in various fields, they still come short in modeling periodicity; they either collapse, diverge or ignore details. In this paper, we introduce a novel framework inspired by Fourier series to generate periodic signals. We first decompose the given signals into multiple sines and cosines and then conditionally generate periodic signals with the output components. We have shown our model efficacy on three tasks: reconstruction, imputation and conditional generation. Our model outperforms baselines in all tasks and shows more stable and refined results.
Weighted Automata and Expressions over Pre-Rational Monoids
Venue: arXiv cs.FL 2021
Year: 2021

The Kleene theorem establishes a fundamental link between automata and expressions over the free monoid. Numerous generalisations of this result exist in the literature. Lifting this result to a weighted setting has been widely studied. Moreover, different monoids can be considered: for instance, two-way automata, and even tree-walking automata, can be described by expressions using the free inverse monoid. In the present work, we aim at combining both research directions and consider weighted extensions of automata and expressions over a class of monoids that we call pre-rational, generalising both the free inverse monoid and graded monoids. The presence of idempotent elements in these pre-rational monoids leads in the weighted setting to consider infinite sums. To handle such sums, we will have to restrict ourselves to rationally additive semirings. Our main result is thus a generalisation of the Kleene theorem for pre-rational monoids and rationally additive semirings. As a corollary, we obtain a class of expressions equivalent to weighted two-way automata, as well as one for tree-walking automata.
A Dynamic Keypoints Selection Network for 6DoF Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

6 DoF poses estimation problem aims to estimate the rotation and translation parameters between two coordinates, such as object world coordinate and camera world coordinate. Although some advances are made with the help of deep learning, how to full use scene information is still a problem. Prior works tackle the problem by pixel-wise feature fusion but need to randomly selecte numerous points from images, which can not satisfy the demands of fast inference simultaneously and accurate pose estimation. In this work, we present a novel deep neural network based on dynamic keypoints selection designed for 6DoF pose estimation from a single RGBD image. Our network includes three parts, instance semantic segmentation, edge points detection and 6DoF pose estimation. Given an RGBD image, our network is trained to predict pixel category and the translation to edge points and center points. Then, a least-square fitting manner is applied to estimate the 6DoF pose parameters. Specifically, we propose a dynamic keypoints selection algorithm to choose keypoints from the foreground feature map. It allows us to leverage geometric and appearance information. During 6DoF pose estimation, we utilize the instance semantic segmentation result to filter out background points and only use foreground points to finish edge points detection and 6DoF pose estimation. Experiments on two commonly used 6DoF estimation benchmark datasets, YCB-Video and LineMoD, demonstrate that our method outperforms the state-of-the-art methods and achieves significant improvements over other same category methods time efficiency.
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
Venue: arXiv cs.CV 2021
Year: 2021

The paper presents a deep neural network-based method for global and local descriptors extraction from a point cloud acquired by a rotating 3D LiDAR. The descriptors can be used for two-stage 6DoF relocalization. First, a course position is retrieved by finding candidates with the closest global descriptor in the database of geo-tagged point clouds. Then, the 6DoF pose between a query point cloud and a database point cloud is estimated by matching local descriptors and using a robust estimator such as RANSAC. Our method has a simple, fully convolutional architecture based on a sparse voxelized representation. It can efficiently extract a global descriptor and a set of keypoints with local descriptors from large point clouds with tens of thousand points. Our code and pretrained models are publicly available on the project website.
The privacy protection effectiveness of the video conference platforms' virtual background and the privacy concerns from the end-users
Venue: arXiv cs.HC 2021
Year: 2021

Due to the abrupt arise of pandemic worldwide, the video conferencing platforms are becoming ubiquitously available and being embedded into either various digital devices or the collaborative daily work. Even though the service provider has designed many security functions to protect individual's privacy, such as virtual background (VB), it still remains to be explored that how the instability of VB leaks users' privacy or impacts their mentality and behaviours. In order to understand and locate implications for the contextual of the end-users' privacy awareness and its mental model, we will conduct survey and interviews for users as the first stage research. We will raise conceptual challenges in terms of the designing safety and stable VB, as well as provide design suggestions.
Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery
Venue: arXiv cs.DC 2021
Year: 2021

Wing and Tip decomposition construct a hierarchy of butterfly-dense edge and vertex induced bipartite subgraphs, respectively. They have applications in several domains including e-commerce, recommendation systems and document analysis. Existing decomposition algorithms use a bottom-up approach that constructs the hierarchy in an increasing order of subgraph density. They iteratively peel the entities with minimum butterfly count i.e. remove them from the graph and update the butterfly count of other entities. However, the amount of butterflies in large bipartite graphs makes bottom-up peeling computationally demanding. Furthermore, the strict order of peeling entities results in a numerous sequentially dependent iterations, which makes parallelization challenging. In this paper, we propose a novel Parallel Bipartite Network peelinG (PBNG) framework which adopts a two-phased peeling approach to relax the order of peeling, and in turn, reduce synchronization. The first phase divides the decomposition hierarchy into several partitions using very few peeling iterations. The second phase concurrently processes these partitions to generate the final hierarchy, and requires no global synchronization. The two-phased peeling further enables batching optimizations that dramatically improve the computational efficiency. We empirically evaluate PBNG using several real-world bipartite graphs. Compared to the state-of-the-art frameworks and decomposition algorithms, PBNG achieves up to four orders of magnitude reduction in synchronization and two orders of magnitude speedup, respectively. We also present the first decomposition results of some of the largest public real-world datasets, which PBNG can peel in few minutes but existing algorithms fail to process even in several days.
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a novel method, X-Distill, to improve the self-supervised training of monocular depth via cross-task knowledge distillation from semantic segmentation to depth estimation. More specifically, during training, we utilize a pretrained semantic segmentation teacher network and transfer its semantic knowledge to the depth network. In order to enable such knowledge distillation across two different visual tasks, we introduce a small, trainable network that translates the predicted depth map to a semantic segmentation map, which can then be supervised by the teacher network. In this way, this small network enables the backpropagation from the semantic segmentation teacher's supervision to the depth network during training. In addition, since the commonly used object classes in semantic segmentation are not directly transferable to depth, we study the visual and geometric characteristics of the objects and design a new way of grouping them that can be shared by both tasks. It is noteworthy that our approach only modifies the training process and does not incur additional computation during inference. We extensively evaluate the efficacy of our proposed approach on the standard KITTI benchmark and compare it with the latest state of the art. We further test the generalizability of our approach on Make3D. Overall, the results show that our approach significantly improves the depth estimation accuracy and outperforms the state of the art.
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length. The challenge increases greatly in the multi-agent reinforcement learning (MARL) setting where the transition probabilities, observation kernel, and reward function are unknown. Here, we develop a general compression framework with approximate common and private state representations, based on which decentralized policies can be constructed. We derive the optimality gap of executing dynamic programming (DP) with the approximate states in terms of the approximation error parameters and the remaining time steps. When the compression is exact (no error), the resulting DP is equivalent to the one in existing work. Our general framework generalizes a number of methods proposed in the literature. The results shed light on designing practically useful deep-MARL network structures under the "centralized learning distributed execution" scheme.
Least Square Calibration for Peer Review
Venue: arXiv cs.LG 2021
Year: 2021

Peer review systems such as conference paper review often suffer from the issue of miscalibration. Previous works on peer review calibration usually only use the ordinal information or assume simplistic reviewer scoring functions such as linear functions. In practice, applications like academic conferences often rely on manual methods, such as open discussions, to mitigate miscalibration. It remains an important question to develop algorithms that can handle different types of miscalibrations based on available prior knowledge. In this paper, we propose a flexible framework, namely least square calibration (LSC), for selecting top candidates from peer ratings. Our framework provably performs perfect calibration from noiseless linear scoring functions under mild assumptions, yet also provides competitive calibration results when the scoring function is from broader classes beyond linear functions and with arbitrary noise. On our synthetic dataset, we empirically demonstrate that our algorithm consistently outperforms the baseline which select top papers based on the highest average ratings.
DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021
Venue: arXiv cs.SD 2021
Year: 2021

This paper describes the Microsoft end-to-end neural text to speech (TTS) system: DelightfulTTS for Blizzard Challenge 2021. The goal of this challenge is to synthesize natural and high-quality speech from text, and we approach this goal in two perspectives: The first is to directly model and generate waveform in 48 kHz sampling rate, which brings higher perception quality than previous systems with 16 kHz or 24 kHz sampling rate; The second is to model the variation information in speech through a systematic design, which improves the prosody and naturalness. Specifically, for 48 kHz modeling, we predict 16 kHz mel-spectrogram in acoustic model, and propose a vocoder called HiFiNet to directly generate 48 kHz waveform from predicted 16 kHz mel-spectrogram, which can better trade off training efficiency, modelling stability and voice quality. We model variation information systematically from both explicit (speaker ID, language ID, pitch and duration) and implicit (utterance-level and phoneme-level prosody) perspectives: 1) For speaker and language ID, we use lookup embedding in training and inference; 2) For pitch and duration, we extract the values from paired text-speech data in training and use two predictors to predict the values in inference; 3) For utterance-level and phoneme-level prosody, we use two reference encoders to extract the values in training, and use two separate predictors to predict the values in inference. Additionally, we introduce an improved Conformer block to better model the local and global dependency in acoustic model. For task SH1, DelightfulTTS achieves 4.17 mean score in MOS test and 4.35 in SMOS test, which indicates the effectiveness of our proposed system
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation
Venue: arXiv cs.CV 2021
Year: 2021

Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. The generalization ability of face presentation attack detection models to unseen attacks has become a key issue for real-world deployment, which can be improved when models are trained with face images from different input distributions and different types of spoof attacks. In reality, due to legal and privacy issues, training data (both real face images and spoof images) are not allowed to be directly shared between different data sources. In this paper, to circumvent this challenge, we propose a Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation framework, with the aim of enhancing the generalization ability of fPAD models in both training and testing phase while preserving data privacy. In the training phase, the proposed framework exploits the federated learning technique, which simultaneously takes advantage of rich fPAD information available at different data sources by aggregating model updates from them without accessing their private data. To further boost the generalization ability, in the testing phase, we explore test-time adaptation by minimizing the entropy of fPAD model prediction on the testing data, which alleviates the domain gap between training and testing data and thus reduces the generalization error of a fPAD model. We introduce the experimental setting to evaluate the proposed framework and carry out extensive experiments to provide various insights about the proposed method for fPAD.
Ultra Light OCR Competition Technical Report
Venue: arXiv cs.CV 2021
Year: 2021

Ultra Light OCR Competition is a Chinese scene text recognition competition jointly organized by CSIG (China Society of Image and Graphics) and Baidu, Inc. In addition to focusing on common problems in Chinese scene text recognition, such as long text length and massive characters, we need to balance the trade-off of model scale and accuracy since the model size limitation in the competition is 10M. From experiments in aspects of data, model, training, etc, we proposed a general and effective method for Chinese scene text recognition, which got us second place among over 100 teams with accuracy 0.817 in TestB dataset. The code is available at https://aistudio.baidu.com/aistudio/projectdetail/2159102.
Orthogonal variance-based feature selection for intrusion detection systems
Venue: arXiv cs.CR 2021
Year: 2021

In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.
Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation
Venue: arXiv cs.CV 2021
Year: 2021

Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set Domain Adaptation (OSDA) assumes there exist additional classes in the target domain but not present in the source domain. To solve such a domain adaptation problem, our proposed method learns discriminative common subspaces for the source and target domains using a novel Open-Set Locality Preserving Projection (OSLPP) algorithm. The source and target domain data are aligned in the learned common spaces class-wisely. To handle the open-set classification problem, our method progressively selects target samples to be pseudo-labelled as known classes and rejects the outliers if they are detected as from unknown classes. The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes. The common subspace learning and the pseudo-labelled sample selection/rejection facilitate each other in an iterative learning framework and achieves state-of-the-art performance on benchmark datasets Office-31 and Office-Home with the average HOS of 87.4% and 67.0% respectively.
Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network
Venue: arXiv cs.CV 2021
Year: 2021

In our research, an adaptive structural learning method of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) has been developed as one of prominent deep learning models. The neuron generation-annihilation in RBM and layer generation algorithms in DBN make an optimal network structure for given input during the learning. In this paper, our model is applied to an automatic recognition method of road network system, called RoadTracer. RoadTracer can generate a road map on the ground surface from aerial photograph data. In the iterative search algorithm, a CNN is trained to find network graph connectivities between roads with high detection capability. However, the system takes a long calculation time for not only the training phase but also the inference phase, then it may not realize high accuracy. In order to improve the accuracy and the calculation time, our Adaptive DBN was implemented on the RoadTracer instead of the CNN. The performance of our developed model was evaluated on a satellite image in the suburban area, Japan. Our Adaptive DBN had an advantage of not only the detection accuracy but also the inference time compared with the conventional CNN in the experiment results.
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks
Venue: arXiv cs.CV 2021
Year: 2021

Knowledge transfer using convolutional neural networks (CNNs) can help efficiently train a CNN with fewer parameters or maximize the generalization performance under limited supervision. To enable a more efficient transfer of pretrained knowledge under relaxed conditions, we propose a simple yet powerful knowledge transfer methodology without any restrictions regarding the network structure or dataset used, namely self-supervised knowledge transfer (SSKT), via loosely supervised auxiliary tasks. For this, we devise a training methodology that transfers previously learned knowledge to the current training process as an auxiliary task for the target task through self-supervision using a soft label. The SSKT is independent of the network structure and dataset, and is trained differently from existing knowledge transfer methods; hence, it has an advantage in that the prior knowledge acquired from various tasks can be naturally transferred during the training process to the target task. Furthermore, it can improve the generalization performance on most datasets through the proposed knowledge transfer between different problem domains from multiple source networks. SSKT outperforms the other transfer learning methods (KD, DML, and MAXL) through experiments under various knowledge transfer settings. The source code will be made available to the public.
On the chromatic number of a family of odd hole free graphs
Venue: arXiv cs.DM 2021
Year: 2021

A hole is an induced cycle of length at least 4, and an odd hole is a hole of odd length. A full house is a graph composed by a vertex adjacent to both ends of an edge in $K_4$ . Let $H$ be the complement of a cycle on 7 vertices. Chudnovsky et al [6] proved that every (odd hole, $K_4$)-free graph is 4-colorable and is 3-colorable if it does not has $H$ as an induced subgraph. In this paper, we use the proving technique of Chudnovsky et al to generalize this conclusion to (odd hole, full house)-free graphs, and prove that for (odd hole, full house)-free graph $G$, $χ(G)\le ω(G)+1$, and the equality holds if and only if $ω(G)=3$ and $G$ has $H$ as an induced subgraph.