Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs

Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. Neural Operators, generations of neural networks with capability of learning maps between infinite-dimensional spaces, are strong tools for solving parametric PDEs. However, they lack the ability to modeling SPDEs which usually have poor regularity due to the driving noise. As the theory of regularity structure has achieved great successes in analyzing SPDEs and provides the concept model feature vectors that well-approximate SPDEs' solutions, we propose the Neural Operator with Regularity Structure (NORS) which incorporates the feature vectors for modeling dynamics driven by SPDEs. We conduct experiments on various of SPDEs including the dynamic Phi41 model and the 2d stochastic Navier-Stokes equation, and the results demonstrate that the NORS is resolution-invariant, efficient, and achieves one order of magnitude lower error with a modest amount of data.

Blind Orthogonal Least Squares based Compressive Spectrum Sensing

Compressive spectrum sensing (CSS) has been widely studied in wideband cognitive radios, benefiting from the reduction of sampling rate via compressive sensing (CS) technology. However, the sensing performance of most existing CSS excessively relies on the prior information such as spectrum sparsity or noise variance. Thus, a key challenge in practical CSS is how to work effectively even in the absence of such information. In this paper, we propose a blind orthogonal least squares based CSS algorithm (B-OLS-CSS), which functions properly without the requirement of prior information. Specifically, we develop a novel blind stopping rule for the OLS algorithm based on its probabilistic recovery condition. This innovative rule gets rid of the need of the spectrum sparsity or noise information, but only requires the computational-feasible mutual incoherence property of the given measurement matrix. Our theoretical analysis indicates that the signal-to-noise ratio required by the proposed B-OLS-CSS for achieving a certain sensing accuracy is relaxed than that by the benchmark CSS using the OMP algorithm, which is verified by extensive simulation results.

Accurate Portraits of Scientific Resources and Knowledge Service Components

With the advent of the cloud computing era, the cost of creating, capturing and managing information has gradually decreased. The amount of data in the Internet is also showing explosive growth, and more and more scientific and technological resources are uploaded to the network. Different from news and social media data ubiquitous in the Internet, the main body of scientific and technological resources is composed of academic-style resources or entities such as papers, patents, authors, and research institutions. There is a rich relationship network between resources, from which a large amount of cutting-edge scientific and technological information can be mined. There are a large number of management and classification standards for existing scientific and technological resources, but these standards are difficult to completely cover all entities and associations of scientific and technological resources, and cannot accurately extract important information contained in scientific and technological resources. How to construct a complete and accurate representation of scientific and technological resources from structured and unstructured reports and texts in the network, and how to tap the potential value of scientific and technological resources is an urgent problem. The solution is to construct accurate portraits of scientific and technological resources in combination with knowledge graph related technologies.

PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks

Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs that contain the adversary trigger. The major challenge for defending against these attacks is that only the attacker knows the secret trigger and the target class. The problem is further exacerbated by the recent introduction of "Hidden Triggers", where the triggers are carefully fused into the input, bypassing detection by human inspection and causing backdoor identification through anomaly detection to fail. To defend against such imperceptible attacks, in this work we systematically analyze how representations, i.e., the set of neuron activations for a given DNN when using the training data as inputs, are affected by backdoor attacks. We propose PiDAn, an algorithm based on coherence optimization purifying the poisoned data. Our analysis shows that representations of poisoned data and authentic data in the target class are still embedded in different linear subspaces, which implies that they show different coherence with some latent spaces. Based on this observation, the proposed PiDAn algorithm learns a sample-wise weight vector to maximize the projected coherence of weighted samples, where we demonstrate that the learned weight vector has a natural "grouping effect" and is distinguishable between authentic data and poisoned data. This enables the systematic detection and mitigation of backdoor attacks. Based on our theoretical analysis and experimental results, we demonstrate the effectiveness of PiDAn in defending against backdoor attacks that use different settings of poisoned samples on GTSRB and ILSVRC2012 datasets. Our PiDAn algorithm can detect more than 90% infected classes and identify 95% poisoned samples.

A Visual Navigation Perspective for Category-Level Object Pose Estimation

This paper studies category-level object pose estimation based on a single monocular image. Recent advances in pose-aware generative models have paved the way for addressing this challenging task using analysis-by-synthesis. The idea is to sequentially update a set of latent variables, e.g., pose, shape, and appearance, of the generative model until the generated image best agrees with the observation. However, convergence and efficiency are two challenges of this inference procedure. In this paper, we take a deeper look at the inference of analysis-by-synthesis from the perspective of visual navigation, and investigate what is a good navigation policy for this specific task. We evaluate three different strategies, including gradient descent, reinforcement learning and imitation learning, via thorough comparisons in terms of convergence, robustness and efficiency. Moreover, we show that a simple hybrid approach leads to an effective and efficient solution. We further compare these strategies to state-of-the-art methods, and demonstrate superior performance on synthetic and real-world datasets leveraging off-the-shelf pose-aware generative models.

Academic Resource Text Level Multi-label Classification based on Attention

Hierarchical multi-label academic text classification (HMTC) is to assign academic texts into a hierarchically structured labeling system. We propose an attention-based hierarchical multi-label classification algorithm of academic texts (AHMCA) by integrating features such as text, keywords, and hierarchical structure, the academic documents are classified into the most relevant categories. We utilize word2vec and BiLSTM to obtain embedding and latent vector representations of text, keywords, and hierarchies. We use hierarchical attention mechanism to capture the associations between keywords, label hierarchies, and text word vectors to generate hierarchical-specific document embedding vectors to replace the original text embeddings in HMCN-F. The experimental results on the academic text dataset demonstrate the effectiveness of the AHMCA algorithm.

FUTR3D: A Unified Sensor Fusion Framework for 3D Detection

Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Existing multi-modal 3D detection models usually involve customized designs depending on the sensor combinations or setups. In this work, we propose the first unified end-to-end sensor fusion framework for 3D detection, named FUTR3D, which can be used in (almost) any sensor configuration. FUTR3D employs a query-based Modality-Agnostic Feature Sampler (MAFS), together with a transformer decoder with a set-to-set loss for 3D detection, thus avoiding using late fusion heuristics and post-processing tricks. We validate the effectiveness of our framework on various combinations of cameras, low-resolution LiDARs, high-resolution LiDARs, and Radars. On NuScenes dataset, FUTR3D achieves better performance over specifically designed methods across different sensor combinations. Moreover, FUTR3D achieves great flexibility with different sensor configurations and enables low-cost autonomous driving. For example, only using a 4-beam LiDAR with cameras, FUTR3D (56.8 mAP) achieves on par performance with state-of-the-art 3D detection model CenterPoint (56.6 mAP) using a 32-beam LiDAR.

DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow

Accurate LiDAR-camera extrinsic calibration is a precondition for many multi-sensor systems in mobile robots. Most calibration methods rely on laborious manual operations and calibration targets. While working online, the calibration methods should be able to extract information from the environment to construct the cross-modal data association. Convolutional neural networks (CNNs) have powerful feature extraction ability and have been used for calibration. However, most of the past methods solve the extrinsic as a regression task, without considering the geometric constraints involved. In this paper, we propose a novel end-to-end extrinsic calibration method named DXQ-Net, using a differentiable pose estimation module for generalization. We formulate a probabilistic model for LiDAR-camera calibration flow, yielding a prediction of uncertainty to measure the quality of LiDAR-camera data association. Testing experiments illustrate that our method achieves a competitive with other methods for the translation component and state-of-the-art performance for the rotation component. Generalization experiments illustrate that the generalization performance of our method is significantly better than other deep learning-based methods.

CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning

Generating artistic portraits is a challenging problem in computer vision. Existing portrait stylization models that generate good quality results are based on Image-to-Image Translation and require abundant data from both source and target domains. However, without enough data, these methods would result in overfitting. In this work, we propose CtlGAN, a new few-shot artistic portraits generation model with a novel contrastive transfer learning strategy. We adapt a pretrained StyleGAN in the source domain to a target artistic domain with no more than 10 artistic faces. To reduce overfitting to the few training examples, we introduce a novel Cross-Domain Triplet loss which explicitly encourages the target instances generated from different latent codes to be distinguishable. We propose a new encoder which embeds real faces into Z+ space and proposes a dual-path training strategy to better cope with the adapted decoder and eliminate the artifacts. Extensive qualitative, quantitative comparisons and a user study show our method significantly outperforms state-of-the-arts under 10-shot and 1-shot settings and generates high quality artistic portraits. The code will be made publicly available.

Least Square Estimation Network for Depth Completion

Depth completion is a fundamental task in computer vision and robotics research. Many previous works complete the dense depth map with neural networks directly but most of them are non-interpretable and can not generalize to different situations well. In this paper, we propose an effective image representation method for depth completion tasks. The input of our system is a monocular camera frame and the synchronous sparse depth map. The output of our system is a dense per-pixel depth map of the frame. First we use a neural network to transform each pixel into a feature vector, which we call base functions. Then we pick out the known pixels' base functions and their depth values. We use a linear least square algorithm to fit the base functions and the depth values. Then we get the weights estimated from the least square algorithm. Finally, we apply the weights to the whole image and predict the final depth map. Our method is interpretable so it can generalize well. Experiments show that our results beat the state-of-the-art on NYU-Depth-V2 dataset both in accuracy and runtime. Moreover, experiments show that our method can generalize well on different numbers of sparse points and different datasets.