pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Features Guidance Network for partial-to-partial point cloud registration
Authors: Hongyuan Wang, Xiang Liu, Wen Kang, Zhiqiang Yan, Bingwen Wang, Qianhao Ning
Date: 2020-11-24 00:00:00
Tasks: Computational Efficiency, Point Cloud Registration

To eliminate the problems of large dimensional differences, big semantic gap, and mutual interference caused by hybrid features, in this paper, we propose a novel Multi-Features Guidance Network for partial-to-partial point cloud registration(MFG). The proposed network mainly includes four parts: keypoints' feature extraction, correspondences searching, correspondences credibility computation, and SVD, among which correspondences searching and correspondence credibility computation are the cores of the network. Unlike the previous work, we utilize the shape features and the spatial coordinates to guide correspondences search independently and fusing the matching results to obtain the final matching matrix. In the correspondences credibility computation module, based on the conflicted relationship between the features matching matrix and the coordinates matching matrix, we score the reliability for each correspondence, which can reduce the impact of mismatched or non-matched points. Experimental results show that our network outperforms the current state-of-the-art while maintaining computational efficiency.
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
Authors: Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
Date: 2020-11-24 00:00:00
Proceeding: ECCV 2020 8
Tasks: Anomaly Detection, Clustering, Event Detection, Supervised Anomaly Detection, Weakly-supervised Anomaly Detection, Weakly-supervised Learning

Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
Fuzzy Stochastic Timed Petri Nets for Causal properties representation
Authors: Alejandro Sobrino, Eduardo C. Garrido-Merchan, Cristina Puente
Date: 2020-11-24 00:00:00

Imagery is frequently used to model, represent and communicate knowledge. In particular, graphs are one of the most powerful tools, being able to represent relations between objects. Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. A causal graph is a skeletal picture, showing causal associations and impact between entities. Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets. Causality is often defined in terms of precedence (the cause precedes the effect), concurrency (often, an effect is provoked simultaneously by two or more causes), circularity (a cause provokes the effect and the effect reinforces the cause) and imprecision (the presence of the cause favors the effect, but not necessarily causes it). We will show that, even though the traditional graphical models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them. To approach that gap, we will introduce Fuzzy Stochastic Timed Petri Nets as a graphical tool able to represent time, co-occurrence, looping and imprecision in causal flow.
Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis
Authors: Michael A. Lepori, R. Thomas McCoy
Date: 2020-11-24 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentence

As the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representations? We introduce an approach to address this question using Representational Similarity Analysis (RSA). As case studies, we investigate the degree to which a verb embedding encodes the verb's subject, a pronoun embedding encodes the pronoun's antecedent, and a full-sentence representation encodes the sentence's head word (as determined by a dependency parse). In all cases, we show that BERT's contextualized embeddings reflect the linguistic dependency being studied, and that BERT encodes these dependencies to a greater degree than it encodes less linguistically-salient controls. These results demonstrate the ability of our approach to adjudicate between hypotheses about which aspects of context are encoded in representations of language.
Infrared small target detection based on isotropic constraint under complex background
Authors: Fan Wang
Date: 2020-11-24 00:00:00

Infrared search and tracking (IRST) system has been widely concerned and applied in the area of national defence. Small target detection under complex background is a very challenging task in the development of system algorithm. Low signal-to-clutter ratio (SCR) of target and the interference caused by irregular background clutter make it difficult to get an accurate result. In this paper, small targets are considered to have two characteristics of high contrast and isotropy, and we propose a multilayer gray difference (MGD) method constrained by isotropy. Firstly, the suspected regions are obtained through MGD, and then the eigenvalues of the original image's Hessian matrix are calculated to obtain the isotropy parameter of each region. Finally, those regions do not meet the isotropic constraint condition are suppressed. Experiments show that the proposed method is effective and superior to several common methods in terms of signal-to-clutter ratio gain (SCRG) and receiver operating characteristic (ROC) curve.
Effective and Sparse Count-Sketch via k-means clustering
Authors: YuHan Wang, Zijian Lei, Liang Lan
Date: 2020-11-24 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Count-sketch is a popular matrix sketching algorithm that can produce a sketch of an input data matrix X in O(nnz(X))time where nnz(X) denotes the number of non-zero entries in X. The sketched matrix will be much smaller than X while preserving most of its properties. Therefore, count-sketch is widely used for addressing high-dimensionality challenge in machine learning. However, there are two main limitations of count-sketch: (1) The sketching matrix used count-sketch is generated randomly which does not consider any intrinsic data properties of X. This data-oblivious matrix sketching method could produce a bad sketched matrix which will result in low accuracy for subsequent machine learning tasks (e.g.classification); (2) For highly sparse input data, count-sketch could produce a dense sketched data matrix. This dense sketch matrix could make the subsequent machine learning tasks more computationally expensive than on the original sparse data X. To address these two limitations, we first show an interesting connection between count-sketch and k-means clustering by analyzing the reconstruction error of the count-sketch method. Based on our analysis, we propose to reduce the reconstruction error of count-sketch by using k-means clustering algorithm to obtain the low-dimensional sketched matrix. In addition, we propose to solve k-mean clustering using gradient descent with -L1 ball projection to produce a sparse sketched matrix. Our experimental results based on six real-life classification datasets have demonstrated that our proposed method achieves higher accuracy than the original count-sketch and other popular matrix sketching algorithms. Our results also demonstrate that our method produces a sparser sketched data matrix than other methods and therefore the prediction cost of our method will be smaller than other matrix sketching methods.
Efficient Sampling for Predictor-Based Neural Architecture Search
Authors: Lukas Mauch, Stephen Tiedemann, Javier Alonso Garcia, Bac Nguyen Cong, Kazuki Yoshiyama, Fabien Cardinaux, Thomas Kemp
Date: 2020-11-24 00:00:00
Tasks: Neural Architecture Search

Recently, predictor-based algorithms emerged as a promising approach for neural architecture search (NAS). For NAS, we typically have to calculate the validation accuracy of a large number of Deep Neural Networks (DNNs), what is computationally complex. Predictor-based NAS algorithms address this problem. They train a proxy model that can infer the validation accuracy of DNNs directly from their network structure. During optimization, the proxy can be used to narrow down the number of architectures for which the true validation accuracy must be computed, what makes predictor-based algorithms sample efficient. Usually, we compute the proxy for all DNNs in the network search space and pick those that maximize the proxy as candidates for optimization. However, that is intractable in practice, because the search spaces are often very large and contain billions of network architectures. The contributions of this paper are threefold: 1) We define a sample efficiency gain to compare different predictor-based NAS algorithms. 2) We conduct experiments on the NASBench-101 dataset and show that the sample efficiency of predictor-based algorithms decreases dramatically if the proxy is only computed for a subset of the search space. 3) We show that if we choose the subset of the search space on which the proxy is evaluated in a smart way, the sample efficiency of the original predictor-based algorithm that has access to the full search space can be regained. This is an important step to make predictor-based NAS algorithms useful, in practice.
Revisiting Pixel-Wise Supervision for Face Anti-Spoofing
Authors: Zitong Yu, Xiaobai Li, Jingang Shi, Zhaoqiang Xia, Guoying Zhao
Date: 2020-11-24 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from the presentation attacks (PAs). As more and more realistic PAs with novel types spring up, it is necessary to develop robust algorithms for detecting unknown attacks even in unseen scenarios. However, deep models supervised by traditional binary loss (e.g., `0' for bonafide vs. `1' for PAs) are weak in describing intrinsic and discriminative spoofing patterns. Recently, pixel-wise supervision has been proposed for the FAS task, intending to provide more fine-grained pixel/patch-level cues. In this paper, we firstly give a comprehensive review and analysis about the existing pixel-wise supervision methods for FAS. Then we propose a novel pyramid supervision, which guides deep models to learn both local details and global semantics from multi-scale spatial context. Extensive experiments are performed on five FAS benchmark datasets to show that, without bells and whistles, the proposed pyramid supervision could not only improve the performance beyond existing pixel-wise supervision frameworks, but also enhance the model's interpretability (i.e., locating the patch-level positions of PAs more reasonably). Furthermore, elaborate studies are conducted for exploring the efficacy of different architecture configurations with two kinds of pixel-wise supervisions (binary mask and depth map supervisions), which provides inspirable insights for future architecture/supervision design.
Adversarial Generation of Continuous Images
Authors: Ivan Skorokhodov, Savva Ignatyev, Mohamed Elhoseiny
Date: 2020-11-24 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Generation

In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) - an MLP that predicts an RGB pixel value given its (x,y) coordinate. In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and use them to build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs for image generation were limited to MNIST-like datasets and do not scale to complex real-world data. Our proposed INR-GAN architecture improves the performance of continuous image generators by several times, greatly reducing the gap between continuous image GANs and pixel-based ones. Apart from that, we explore several exciting properties of the INR-based decoders, like out-of-the-box superresolution, meaningful image-space interpolation, accelerated inference of low-resolution images, an ability to extrapolate outside of image boundaries, and strong geometric prior. The project page is located at https://universome.github.io/inr-gan.
SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation
Authors: Thomas Verelst, Tinne Tuytelaars
Date: 2020-11-24 00:00:00
Tasks: Real-Time Semantic Segmentation, Semantic Segmentation

SegBlocks reduces the computational cost of existing neural networks, by dynamically adjusting the processing resolution of image regions based on their complexity. Our method splits an image into blocks and downsamples blocks of low complexity, reducing the number of operations and memory consumption. A lightweight policy network, selecting the complex regions, is trained using reinforcement learning. In addition, we introduce several modules implemented in CUDA to process images in blocks. Most important, our novel BlockPad module prevents the feature discontinuities at block borders of which existing methods suffer, while keeping memory consumption under control. Our experiments on Cityscapes, Camvid and Mapillary Vistas datasets for semantic segmentation show that dynamically processing images offers a better accuracy versus complexity trade-off compared to static baselines of similar complexity. For instance, our method reduces the number of floating-point operations of SwiftNet-RN18 by 60% and increases the inference speed by 50%, with only 0.3% decrease in mIoU accuracy on Cityscapes.
RIN: Textured Human Model Recovery and Imitation with a Single Image
Authors: Haoxi Ran, Guangfu Wang, Li Lu
Date: 2020-11-24 00:00:00
Tasks: Translation

Human imitation has become topical recently, driven by GAN's ability to disentangle human pose and body content. However, the latest methods hardly focus on 3D information, and to avoid self-occlusion, a massive amount of input images are needed. In this paper, we propose RIN, a novel volume-based framework for reconstructing a textured 3D model from a single picture and imitating a subject with the generated model. Specifically, to estimate most of the human texture, we propose a U-Net-like front-to-back translation network. With both front and back images input, the textured volume recovery module allows us to color a volumetric human. A sequence of 3D poses then guides the colored volume via Flowable Disentangle Networks as a volume-to-volume translation task. To project volumes to a 2D plane during training, we design a differentiable depth-aware renderer. Our experiments demonstrate that our volume-based model is adequate for human imitation, and the back view can be estimated reliably using our network. While prior works based on either 2D pose or semantic map often fail for the unstable appearance of a human, our framework can still produce concrete results, which are competitive to those imagined from multi-view input.
Multi-Decoder DPRNN: High Accuracy Source Counting and Separation
Authors: Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson
Date: 2020-11-24 00:00:00
Tasks: Decoder, Speech Separation, Vocal Bursts Intensity Prediction

We propose an end-to-end trainable approach to single-channel speech separation with unknown number of speakers. Our approach extends the MulCat source separation backbone with additional output heads: a count-head to infer the number of speakers, and decoder-heads for reconstructing the original signals. Beyond the model, we also propose a metric on how to evaluate source separation with variable number of speakers. Specifically, we cleared up the issue on how to evaluate the quality when the ground-truth hasmore or less speakers than the ones predicted by the model. We evaluate our approach on the WSJ0-mix datasets, with mixtures up to five speakers. We demonstrate that our approach outperforms state-of-the-art in counting the number of speakers and remains competitive in quality of reconstructed signals.
Argument from Old Man's View: Assessing Social Bias in Argumentation
Authors: Maximilian Spliethöver, Henning Wachsmuth
Date: 2020-11-24 00:00:00
Tasks: Word Embeddings

Social bias in language - towards genders, ethnicities, ages, and other social groups - poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. So far, however, little attention has been paid to bias in computational argumentation. In this paper, we study the existence of social biases in large English debate portals. In particular, we train word embedding models on portal-specific corpora and systematically evaluate their bias using WEAT, an existing metric to measure bias in word embeddings. In a word co-occurrence analysis, we then investigate causes of bias. The results suggest that all tested debate corpora contain unbalanced and biased data, mostly in favor of male people with European-American names. Our empirical insights contribute towards an understanding of bias in argumentative data sources.
A More Biologically Plausible Local Learning Rule for ANNs
Authors: Shashi Kant Gupta
Date: 2020-11-24 00:00:00
Tasks: Adversarial Robustness, Binary Classification

The backpropagation algorithm is often debated for its biological plausibility. However, various learning methods for neural architecture have been proposed in search of more biologically plausible learning. Most of them have tried to solve the "weight transport problem" and try to propagate errors backward in the architecture via some alternative methods. In this work, we investigated a slightly different approach that uses only the local information which captures spike timing information with no propagation of errors. The proposed learning rule is derived from the concepts of spike timing dependant plasticity and neuronal association. A preliminary evaluation done on the binary classification of MNIST and IRIS datasets with two hidden layers shows comparable performance with backpropagation. The model learned using this method also shows a possibility of better adversarial robustness against the FGSM attack compared to the model learned through backpropagation of cross-entropy loss. The local nature of learning gives a possibility of large scale distributed and parallel learning in the network. And finally, the proposed method is a more biologically sound method that can probably help in understanding how biological neurons learn different abstractions.
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Authors: Cheng Wang, Carolin Lawrence, Mathias Niepert
Date: 2020-11-24 00:00:00
Proceeding: ICLR 2021 1
Tasks: Out-of-Distribution Detection, reinforcement-learning, Reinforcement Learning (RL), Uncertainty Quantification

Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several times, each time sampling from the recurrent state transition distribution, leading to potentially different results if the model is uncertain. Alongside uncertainty quantification, our proposed method offers several advantages in different settings. The proposed method can (1) learn deterministic and probabilistic automata from data, (2) learn well-calibrated models on real-world classification tasks, (3) improve the performance of out-of-distribution detection, and (4) control the exploration-exploitation trade-off in reinforcement learning.
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition
Authors: Racha Friji, Hassen Drira, Faten Chaieb, Sebastian Kurtek, Hamza Kchok
Date: 2020-11-24 00:00:00
Tasks: Action Recognition, Deep Learning, Skeleton Based Action Recognition

Deep Learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space. In this paper, we propose a geometry aware deep learning approach for skeleton-based action recognition. Skeleton sequences are first modeled as trajectories on Kendall's shape space and then mapped to the linear tangent space. The resulting structured data are then fed to a deep learning architecture, which includes a layer that optimizes over rigid and non rigid transformations of the 3D skeletons, followed by a CNN-LSTM network. The assessment on two large scale skeleton datasets, namely NTU-RGB+D and NTU-RGB+D 120, has proven that proposed approach outperforms existing geometric deep learning methods and is competitive with respect to recently published approaches.
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
Authors: Yang You, Zelin Ye, Yujing Lou, Chengkun Li, Yong-Lu Li, Lizhuang Ma, Weiming Wang, Cewu Lu
Date: 2020-11-24 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Object Detection, object-detection, Object Detection

3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress direct offset towards object centers and box orientations with an additional Multi-Layer-Perceptron network. Both their offset and orientation predictions are not accurate due to the fundamental difficulty in rotation classification. In the work, we disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations. Only LCC and box scales are regressed, while box orientations are generated by a canonical voting scheme. Finally, an LCC-aware back-projection checking algorithm iteratively cuts out bounding boxes from the generated vote maps, with the elimination of false positives. Our model achieves state-of-the-art performance on three standard real-world benchmarks: ScanNet, SceneNN and SUN RGB-D. Our code is available on https://github.com/qq456cvb/CanonicalVoting.