pwc-archive/papers-with-abstracts huggingface dataset:
FocusedDropout for Convolutional Neural Network
Authors: Tianshu Xie, Minghui Liu, Jiali Deng, Xuan Cheng, Xiaomin Wang, Ming Liu
Date: 2021-03-29 00:00:00

In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units. In this paper, we propose a non-random dropout method named FocusedDropout, aiming to make the network focus more on the target. In FocusedDropout, we use a simple but effective way to search for the target-related features, retain these features and discard others, which is contrary to the existing methods. We found that this novel method can improve network performance by making the network more target-focused. Besides, increasing the weight decay while using FocusedDropout can avoid the overfitting and increase accuracy. Experimental results show that even a slight cost, 10\% of batches employing FocusedDropout, can produce a nice performance boost over the baselines on multiple datasets of classification, including CIFAR10, CIFAR100, Tiny Imagenet, and has a good versatility for different CNN models.
Translating Numerical Concepts for PDEs into Neural Architectures
Authors: Tobias Alt, Pascal Peter, Joachim Weickert, Karl Schrader
Date: 2021-03-29 00:00:00

We investigate what can be learned from translating numerical algorithms into neural networks. On the numerical side, we consider explicit, accelerated explicit, and implicit schemes for a general higher order nonlinear diffusion equation in 1D, as well as linear multigrid methods. On the neural network side, we identify corresponding concepts in terms of residual networks (ResNets), recurrent networks, and U-nets. These connections guarantee Euclidean stability of specific ResNets with a transposed convolution layer structure in each block. We present three numerical justifications for skip connections: as time discretisations in explicit schemes, as extrapolation mechanisms for accelerating those methods, and as recurrent connections in fixed point solvers for implicit schemes. Last but not least, we also motivate uncommon design choices such as nonmonotone activation functions. Our findings give a numerical perspective on the success of modern neural network architectures, and they provide design criteria for stable networks.
Collocation Polynomial Neural Forms and Domain Fragmentation for solving Initial Value Problems
Authors: Toni Schneidereit, Michael Breuß
Date: 2021-03-29 00:00:00

Several neural network approaches for solving differential equations employ trial solutions with a feedforward neural network. There are different means to incorporate the trial solution in the construction, for instance one may include them directly in the cost function. Used within the corresponding neural network, the trial solutions define the so-called neural form. Such neural forms represent general, flexible tools by which one may solve various differential equations. In this article we consider time-dependent initial value problems, which require to set up the neural form framework adequately. The neural forms presented up to now in the literature for such a setting can be considered as first order polynomials. In this work we propose to extend the polynomial order of the neural forms. The novel collocation-type construction includes several feedforward neural networks, one for each order. Additionally, we propose the fragmentation of the computational domain into subdomains. The neural forms are solved on each subdomain, whereas the interfacing grid points overlap in order to provide initial values over the whole fragmentation. We illustrate in experiments that the combination of collocation neural forms of higher order and the domain fragmentation allows to solve initial value problems over large domains with high accuracy and reliability.
A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios
Authors: Xudong Chen, Shugong Xu, Qiaobin Ji, Shan Cao
Date: 2021-03-29 00:00:00
Tasks: Face Anti-Spoofing

Face Anti-spoofing (FAS) is a challenging problem due to complex serving scenarios and diverse face presentation attack patterns. Especially when captured images are low-resolution, blurry, and coming from different domains, the performance of FAS will degrade significantly. The existing multi-modal FAS datasets rarely pay attention to the cross-domain problems under deployment scenarios, which is not conducive to the study of model performance. To solve these problems, we explore the fine-grained differences between multi-modal cameras and construct a cross-domain multi-modal FAS dataset under surveillance scenarios called GREAT-FASD-S. Besides, we propose an Attention based Face Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards low-quality face images. It consists of the depthwise separable attention module (DAM) and the multi-modal based feature augment module (MFAM). Our model can achieve state-of-the-art performance on the CASIA-SURF dataset and our proposed GREAT-FASD-S dataset.
Self-Supervised Visibility Learning for Novel View Synthesis
Authors: Yujiao Shi, Hongdong Li, Xin Yu
Date: 2021-03-29 00:00:00
Proceeding: CVPR 2021 1
Tasks: Novel View Synthesis

We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, erroneous geometry estimation will decrease NVS performance as view synthesis highly depends on the quality of estimated scene geometry. In this paper, we propose an end-to-end NVS framework to eliminate the error propagation issue. To be specific, we construct a volume under the target view and design a source-view visibility estimation (SVE) module to determine the visibility of the target-view voxels in each source view. Next, we aggregate the visibility of all source views to achieve a consensus volume. Each voxel in the consensus volume indicates a surface existence probability. Then, we present a soft ray-casting (SRC) mechanism to find the most front surface in the target view (i.e. depth). Specifically, our SRC traverses the consensus volume along viewing rays and then estimates a depth probability distribution. We then warp and aggregate source view pixels to synthesize a novel view based on the estimated source-view visibility and target-view depth. At last, our network is trained in an end-to-end self-supervised fashion, thus significantly alleviating error accumulation in view synthesis. Experimental results demonstrate that our method generates novel views in higher quality compared to the state-of-the-art.
Mining Latent Classes for Few-shot Segmentation
Authors: Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao
Date: 2021-03-29 00:00:00
Proceeding: ICCV 2021 10
Tasks: Few-Shot Semantic Segmentation

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we add an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and foreground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5i and 7.0% mIOU on COCO-20i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://github.com/LiheYoung/MiningFSS.
No frame left behind: Full Video Action Recognition
Authors: Xin Liu, Silvia L. Pintea, Fatemeh Karimi Nejadasl, Olaf Booij, Jan C. van Gemert
Date: 2021-03-29 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Recognition, All, Clustering, Temporal Action Localization

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.
Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training
Authors: Mohammad Azizmalayeri, Mohammad Hossein Rohban
Date: 2021-03-29 00:00:00

Recent improvements in deep learning models and their practical applications have raised concerns about the robustness of these models against adversarial examples. Adversarial training (AT) has been shown effective to reach a robust model against the attack that is used during training. However, it usually fails against other attacks, i.e. the model overfits to the training attack scheme. In this paper, we propose a simple modification to the AT that mitigates the mentioned issue. More specifically, we minimize the perturbation $\ell_p$ norm while maximizing the classification loss in the Lagrangian form. We argue that crafting adversarial examples based on this scheme results in enhanced attack generalization in the learned model. We compare our final model robust accuracy against attacks that were not used during training to closely related state-of-the-art AT methods. This comparison demonstrates that our average robust accuracy against unseen attacks is 5.9% higher in the CIFAR-10 dataset and is 3.2% higher in the ImageNet-100 dataset than corresponding state-of-the-art methods. We also demonstrate that our attack is faster than other attack schemes that are designed for unseen attack generalization, and conclude that it is feasible for large-scale datasets.
Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions
Authors: Xuan Cheng, Tianshu Xie, Xiaomin Wang, Qifeng Weng, Minghui Liu, Jiali Deng, Ming Liu
Date: 2021-03-29 00:00:00
Tasks: image-classification, Image Classification

In this paper, we propose Selective Output Smoothing Regularization, a novel regularization method for training the Convolutional Neural Networks (CNNs). Inspired by the diverse effects on training from different samples, Selective Output Smoothing Regularization improves the performance by encouraging the model to produce equal logits on incorrect classes when dealing with samples that the model classifies correctly and over-confidently. This plug-and-play regularization method can be conveniently incorporated into almost any CNN-based project without extra hassle. Extensive experiments have shown that Selective Output Smoothing Regularization consistently achieves significant improvement in image classification benchmarks, such as CIFAR-100, Tiny ImageNet, ImageNet, and CUB-200-2011. Particularly, our method obtains 77.30% accuracy on ImageNet with ResNet-50, which gains 1.1% than baseline (76.2%). We also empirically demonstrate the ability of our method to make further improvements when combining with other widely used regularization techniques. On Pascal detection, using the SOSR-trained ImageNet classifier as the pretrained model leads to better detection performances.
AlignMixup: Improving Representations By Interpolating Aligned Features
Authors: Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Date: 2021-03-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Data Augmentation, Representation Learning, Style Transfer

Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. Many recent mixup methods focus on cutting and pasting two or more objects into one image, which is more about efficient processing than interpolation. However, how to best interpolate images is not well defined. In this sense, mixup has been connected to autoencoders, because often autoencoders "interpolate well", for instance generating an image that continuously deforms into another. In this work, we revisit mixup from the interpolation perspective and introduce AlignMix, where we geometrically align two images in the feature space. The correspondences allow us to interpolate between two sets of features, while keeping the locations of one set. Interestingly, this gives rise to a situation where mixup retains mostly the geometry or pose of one image and the texture of the other, connecting it to style transfer. More than that, we show that an autoencoder can still improve representation learning under mixup, without the classifier ever seeing decoded images. AlignMix outperforms state-of-the-art mixup methods on five different benchmarks.
Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach
Authors: Shaoyang Wang, Tiejun Lv, Wei Ni, Norman C. Beaulieu, Y. Jay Guo
Date: 2021-03-29 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning (RL)

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.
Robust Reinforcement Learning under model misspecification
Authors: Lebin Yu, Jian Wang, Xudong Zhang
Date: 2021-03-29 00:00:00
Tasks: Adversarial Attack, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has achieved remarkable performance in a wide range of tasks these days. Nevertheless, some unsolved problems limit its applications in real-world control. One of them is model misspecification, a situation where an agent is trained and deployed in environments with different transition dynamics. We propose an novel framework that utilize history trajectory and Partial Observable Markov Decision Process Modeling to deal with this dilemma. Additionally, we put forward an efficient adversarial attack method to assist robust training. Our experiments in four gym domains validate the effectiveness of our framework.
Contextual Scene Augmentation and Synthesis via GSACNet
Authors: Mohammad Keshavarzi, Flaviano Christian Reyes, Ritika Shrivastava, Oladapo Afolabi, Luisa Caldas, Allen Y. Yang
Date: 2021-03-29 00:00:00
Tasks: Data Augmentation, Graph Attention

Indoor scene augmentation has become an emerging topic in the field of computer vision and graphics with applications in augmented and virtual reality. However, current state-of-the-art systems using deep neural networks require large datasets for training. In this paper we introduce GSACNet, a contextual scene augmentation system that can be trained with limited scene priors. GSACNet utilizes a novel parametric data augmentation method combined with a Graph Attention and Siamese network architecture followed by an Autoencoder network to facilitate training with small datasets. We show the effectiveness of our proposed system by conducting ablation and comparative studies with alternative systems on the Matterport3D dataset. Our results indicate that our scene augmentation outperforms prior art in scene synthesis with limited scene priors available.
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton
Authors: Xi Zhang, Xiaolin Wu
Date: 2021-03-29 00:00:00
Proceeding: CVPR 2021 1
Tasks: Compressive Sensing, Image Compression

We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and near a saliency sketch within ROI that are critical to perceptual quality. Only the critical pixels are further sampled by compressive sensing (CS) to form a very compact refinement layer. Another novel CNN method is developed to jointly decode the two compression layers for a much refined reconstruction, while strictly satisfying the transmitted CS constraints on perceptually critical pixels. Extensive experiments demonstrate that the proposed AGDL system advances the state of the art in perception-aware image compression.
Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning
Authors: Xiangyu Zhang, Zhengming Zhang, Luxi Yang
Date: 2021-03-29 00:00:00
Tasks: Computational Efficiency, Graph Neural Network, Representation Learning

Heterogeneous Ultra-Dense Network (HUDN) is one of the vital networking architectures due to its ability to enable higher connectivity density and ultra-high data rates. Rational user association and power control schedule in HUDN can reduce wireless interference. This paper proposes a novel idea for resolving the joint user association and power control problem: the optimal user association and Base Station transmit power can be represented by channel information. Then, we solve this problem by formulating an optimal representation function. We model the HUDNs as a heterogeneous graph and train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning, in which the loss function is composed of the unsupervised part that helps the GNN approach the optimal representation function and the supervised part that utilizes the previous experience to reduce useless exploration. We separate the learning process into two parts, the generalization-representation learning (GRL) part and the specialization-representation learning (SRL) part, which train the GNN for learning representation for generalized scenario quasi-static user distribution scenario, respectively. Simulation results demonstrate that the proposed GRL-based solution has higher computational efficiency than the traditional optimization algorithm, and the performance of SRL outperforms the GRL.
Visual Distant Supervision for Scene Graph Generation
Authors: Yuan YAO, Ao Zhang, Xu Han, Mengdi Li, Cornelius Weber, Zhiyuan Liu, Stefan Wermter, Maosong Sun
Date: 2021-03-29 00:00:00
Proceeding: ICCV 2021 10
Tasks: Graph Generation, Predicate Classification, Relation, Scene Graph Generation

Scene graph generation aims to identify objects and their relations in images, providing structured image representations that can facilitate numerous applications in computer vision. However, scene graph models usually require supervised learning on large quantities of labeled data with intensive human annotation. In this work, we propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data. The intuition is that by aligning commonsense knowledge bases and images, we can automatically create large-scale labeled data to provide distant supervision for visual relation learning. To alleviate the noise in distantly labeled data, we further propose a framework that iteratively estimates the probabilistic relation labels and eliminates the noisy ones. Comprehensive experimental results show that our distantly supervised model outperforms strong weakly supervised and semi-supervised baselines. By further incorporating human-labeled data in a semi-supervised fashion, our model outperforms state-of-the-art fully supervised models by a large margin (e.g., 8.3 micro- and 7.8 macro-recall@50 improvements for predicate classification in Visual Genome evaluation). We make the data and code for this paper publicly available at https://github.com/thunlp/VisualDS.
Embedding API Dependency Graph for Neural Code Generation
Authors: Chen Lyu, Ruyun Wang, Hongyu Zhang, Hanwen Zhang, Songlin Hu
Date: 2021-03-29 00:00:00
Tasks: Code Generation, Decoder, Graph Embedding

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.