pwc-archive/papers-with-abstracts huggingface dataset:
Dual Semantic Fusion Network for Video Object Detection
Authors: Lijian Lin, Haosheng Chen, Honglun Zhang, Jun Liang, Yu Li, Ying Shan, Hanzi Wang
Date: 2020-09-16 00:00:00
Tasks: Object, object-detection, Object Detection, Optical Flow Estimation, Video Object Detection

Video object detection is a tough task due to the deteriorated quality of video sequences captured under complex environments. Currently, this area is dominated by a series of feature enhancement based methods, which distill beneficial semantic information from multiple frames and generate enhanced features through fusing the distilled information. However, the distillation and fusion operations are usually performed at either frame level or instance level with external guidance using additional information, such as optical flow and feature memory. In this work, we propose a dual semantic fusion network (abbreviated as DSFNet) to fully exploit both frame-level and instance-level semantics in a unified fusion framework without external guidance. Moreover, we introduce a geometric similarity measure into the fusion process to alleviate the influence of information distortion caused by noise. As a result, the proposed DSFNet can generate more robust features through the multi-granularity fusion and avoid being affected by the instability of external guidance. To evaluate the proposed DSFNet, we conduct extensive experiments on the ImageNet VID dataset. Notably, the proposed dual semantic fusion network achieves, to the best of our knowledge, the best performance of 84.1\% mAP among the current state-of-the-art video object detectors with ResNet-101 and 85.4\% mAP with ResNeXt-101 without using any post-processing steps.
One head is better than two: a polynomial restriction for propositional definite Horn forgetting
Authors: Paolo Liberatore
Date: 2020-09-16 00:00:00

Logical forgetting is \np-complete even in the simple case of propositional Horn formulae, and may exponentially increase their size. A way to forget is to replace each variable to forget with the body of each clause whose head is the variable. It takes polynomial time in the single-head case: each variable is at most the head of a clause. Some formulae are not single-head but can be made so to simplify forgetting. They are single-head equivalent. The first contribution of this article is the study of a semantical characterization of single-head equivalence. Two necessary conditions are given. They are sufficient when the formula is inequivalent: it makes two sets of variables equivalent only if they are also equivalent to their intersection. All acyclic formulae are inequivalent. The second contribution of this article is an incomplete algorithm for turning a formula single-head. In case of success, forgetting becomes possible in polynomial time and produces a polynomial-size formula, none of which is otherwise guaranteed. The algorithm is complete on inequivalent formulae.
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability
Authors: Ninghao Liu, Yunsong Meng, Xia Hu, Tie Wang, Bo Long
Date: 2020-09-16 00:00:00

Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models. Meanwhile, researchers also try to answer the question that whether the obtained interpretation is faithful in explaining mechanisms behind model prediction? Specifically, (Jain and Wallace, 2019) proposes that "attention is not explanation" by comparing attention interpretation with gradient alternatives. However, it raises a new question that can we safely pick one interpretation method as the ground-truth? If not, on what basis can we compare different interpretation methods? In this work, we propose that it is crucial to have a concrete definition of interpretation before we could evaluate faithfulness of an interpretation. The definition will affect both the algorithm to obtain interpretation and, more importantly, the metric used in evaluation. Through both theoretical and experimental analysis, we find that although interpretation methods perform differently under a certain evaluation metric, such a difference may not result from interpretation quality or faithfulness, but rather the inherent bias of the evaluation metric.
Robust Person Re-Identification through Contextual Mutual Boosting
Authors: Zhikang Wang, Lihuo He, Xinbo Gao, Jane Shen
Date: 2020-09-16 00:00:00
Tasks: Human Parsing, Person Re-Identification, Pose Estimation

Person Re-Identification (Re-ID) has witnessed great advance, driven by the development of deep learning. However, modern person Re-ID is still challenged by background clutter, occlusion and large posture variation which are common in practice. Previous methods tackle these challenges by localizing pedestrians through external cues (e.g., pose estimation, human parsing) or attention mechanism, suffering from high computation cost and increased model complexity. In this paper, we propose the Contextual Mutual Boosting Network (CMBN). It localizes pedestrians and recalibrates features by effectively exploiting contextual information and statistical inference. Firstly, we construct two branches with a shared convolutional frontend to learn the foreground and background features respectively. By enabling interaction between these two branches, they boost the accuracy of the spatial localization mutually. Secondly, starting from a statistical perspective, we propose the Mask Generator that exploits the activation distribution of the transformation matrix for generating the static channel mask to the representations. The mask recalibrates the features to amplify the valuable characteristics and diminish the noise. Finally, we propose the Contextual-Detachment Strategy to optimize the two branches jointly and independently, which further enhances the localization precision. Experiments on the benchmarks demonstrate the superiority of the architecture compared the state-of-the-art.
Graph-to-Sequence Neural Machine Translation
Authors: Sufeng Duan, Hai Zhao, Rui Wang
Date: 2020-09-16 00:00:00
Tasks: Graph-to-Sequence, Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) usually works in a seq2seq learning way by viewing either source or target sentence as a linear sequence of words, which can be regarded as a special case of graph, taking words in the sequence as nodes and relationships between words as edges. In the light of the current NMT models more or less capture graph information among the sequence in a latent way, we present a graph-to-sequence model facilitating explicit graph information capturing. In detail, we propose a graph-based SAN-based NMT model called Graph-Transformer by capturing information of subgraphs of different orders in every layers. Subgraphs are put into different groups according to their orders, and every group of subgraphs respectively reflect different levels of dependency between words. For fusing subgraph representations, we empirically explore three methods which weight different groups of subgraphs of different orders. Results of experiments on WMT14 English-German and IWSLT14 German-English show that our method can effectively boost the Transformer with an improvement of 1.1 BLEU points on WMT14 English-German dataset and 1.0 BLEU points on IWSLT14 German-English dataset.
Pooling Methods in Deep Neural Networks, a Review
Authors: Hossein Gholamalinezhad, Hossein Khosravi
Date: 2020-09-16 00:00:00

Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer. The pooling layer is an important layer that executes the down-sampling on the feature maps coming from the previous layer and produces new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves two main purposes. The first is to reduce the number of parameters or weights, thus lessening the computational cost. The second is to control the overfitting of the network. An ideal pooling method is expected to extract only useful information and discard irrelevant details. There are a lot of methods for the implementation of pooling operation in Deep Neural Networks. In this paper, we reviewed some of the famous and useful pooling methods.
Unsupervised Summarization by Jointly Extracting Sentences and Keywords
Authors: Zongyi Li, Xiaoqing Zheng, Jun He
Date: 2020-09-16 00:00:00
Tasks: Diversity, Document Summarization, Multi-Document Summarization, Sentence, Word Embeddings

We present RepRank, an unsupervised graph-based ranking model for extractive multi-document summarization in which the similarity between words, sentences, and word-to-sentence can be estimated by the distances between their vector representations in a unified vector space. In order to obtain desirable representations, we propose a self-attention based learning method that represent a sentence by the weighted sum of its word embeddings, and the weights are concentrated to those words hopefully better reflecting the content of a document. We show that salient sentences and keywords can be extracted in a joint and mutual reinforcement process using our learned representations, and prove that this process always converges to a unique solution leading to improvement in performance. A variant of absorbing random walk and the corresponding sampling-based algorithm are also described to avoid redundancy and increase diversity in the summaries. Experiment results with multiple benchmark datasets show that RepRank achieved the best or comparable performance in ROUGE.
A Convolutional LSTM based Residual Network for Deepfake Video Detection
Authors: Shahroz Tariq, Sangyup Lee, Simon S. Woo
Date: 2020-09-16 00:00:00
Tasks: DeepFake Detection, Face Swapping, Transfer Learning

In recent years, deep learning-based video manipulation methods have become widely accessible to masses. With little to no effort, people can easily learn how to generate deepfake videos with only a few victims or target images. This creates a significant social problem for everyone whose photos are publicly available on the Internet, especially on social media websites. Several deep learning-based detection methods have been developed to identify these deepfakes. However, these methods lack generalizability, because they perform well only for a specific type of deepfake method. Therefore, those methods are not transferable to detect other deepfake methods. Also, they do not take advantage of the temporal information of the video. In this paper, we addressed these limitations. We developed a Convolutional LSTM based Residual Network (CLRNet), which takes a sequence of consecutive images as an input from a video to learn the temporal information that helps in detecting unnatural looking artifacts that are present between frames of deepfake videos. We also propose a transfer learning-based approach to generalize different deepfake methods. Through rigorous experimentations using the FaceForensics++ dataset, we showed that our method outperforms five of the previously proposed state-of-the-art deepfake detection methods by better generalizing at detecting different deepfake methods using the same model.
Accurate and efficient Simulation of very high-dimensional Neural Mass Models with distributed-delay Connectome Tensors
Authors: A. González-Mitjans, D. Paz-Linares, A. Areces-Gonzalez, M. Li, Y. Wang, ML. Bringas-Vega, P. A Valdés-Sosa
Date: 2020-09-16 00:00:00
Tasks: Computational Efficiency, Electroencephalogram (EEG), Numerical Integration

This paper introduces methods and a novel toolbox that efficiently integrates any high-dimensional Neural Mass Models (NMMs) specified by two essential components. The first is the set of nonlinear Random Differential Equations of the dynamics of each neural mass. The second is the highly sparse three-dimensional Connectome Tensor (CT) that encodes the strength of the connections and the delays of information transfer along the axons of each connection. Semi-analytical integration of the RDE is done with the Local Linearization scheme for each neural mass model, which is the only scheme guaranteeing dynamical fidelity to the original continuous-time nonlinear dynamic. It also seamlessly allows modeling distributed delays CT with any level of complexity or realism, as shown by the Moore-Penrose diagram of the algorithm. This ease of implementation includes models with distributed-delay CTs. We achieve high computational efficiency by using a tensor representation of the model that leverages semi-analytic expressions to integrate the Random Differential Equations (RDEs) underlying the NMM. We discretized the state equation with Local Linearization via an algebraic formulation. This approach increases numerical integration speed and efficiency, a crucial aspect of large-scale NMM simulations. To illustrate the usefulness of the toolbox, we simulate both a single Zetterberg-Jansen-Rit (ZJR) cortical column and an interconnected population of such columns. These examples illustrate the consequence of modifying the CT in these models, especially by introducing distributed delays. We provide an open-source Matlab live script for the toolbox.
Path Planning using Neural A* Search
Authors: Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki
Date: 2020-09-16 00:00:00

We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off. Furthermore, Neural A* successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs. Project page: https://omron-sinicx.github.io/neural-astar/
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles
Authors: Mayank Raj, Ajay Jaiswal, Rohit R. R, Ankita Gupta, Sudeep Kumar Sahoo, Vertika Srivastava, Yeon Hyang Kim
Date: 2020-09-16 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Articles, Classification, General Classification, Multi-class Classification, Propaganda detection

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles"\cite{DaSanMartinoSemeval20task11}. We participated in Task "Technique Classification" (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision
Authors: Sathyanarayanan N. Aakur, Sanjoy Kundu, Nikhil Gunti
Date: 2020-09-16 00:00:00
Tasks: Action Recognition, Domain Adaptation, Novel Object Detection, object-detection, Object Detection, Zero-Shot Learning

Advances in deep learning have enabled the development of models that have exhibited a remarkable tendency to recognize and even localize actions in videos. However, they tend to experience errors when faced with scenes or examples beyond their initial training environment. Hence, they fail to adapt to new domains without significant retraining with large amounts of annotated data. In this paper, we propose to overcome these limitations by moving to an open-world setting by decoupling the ideas of recognition and reasoning. Building upon the compositional representation offered by Grenander's Pattern Theory formalism, we show that attention and commonsense knowledge can be used to enable the self-supervised discovery of novel actions in egocentric videos in an open-world setting, where data from the observed environment (the target domain) is open i.e., the vocabulary is partially known and training examples (both labeled and unlabeled) are not available. We show that our approach can infer and learn novel classes for open vocabulary classification in egocentric videos and novel object detection with zero supervision. Extensive experiments show its competitive performance on two publicly available egocentric action recognition datasets (GTEA Gaze and GTEA Gaze+) under open-world conditions.
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Authors: Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing
Date: 2020-09-16 00:00:00
Tasks: Computed Tomography (CT), Image Generation, Medical Diagnosis, Metal Artifact Reduction

Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this paper, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.
Answering Any-hop Open-domain Questions with Iterative Document Reranking
Authors: Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song
Date: 2020-09-16 00:00:00
Tasks: Multi-hop Question Answering, Natural Questions, Open-Domain Question Answering, Question Answering, Reranking, Retrieval

Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also, multi-step document retrieval often incurs higher number of relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. To improve the retrieval accuracy, we propose a graph-based reranking model that perform multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA.
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
Authors: Qiang Fu, Jialong Wang, Hongshan Yu, Islam Ali, Feng Guo, Yijia He, Hong Zhang
Date: 2020-09-16 00:00:00
Tasks: Pose Estimation

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}. We observe that current works use the LSD \cite{lsd} algorithm to extract line features; however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, a modified LSD algorithm is presented by studying a hidden parameter tuning and length rejection strategy. The modified LSD can run at least three times as fast as LSD. Further, by representing space lines with the Pl\"{u}cker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16\% less than that of VINS-Mono at the same pose update frequency. %For the benefit of the community, The source code of our method is available at: https://github.com/cnqiangfu/PL-VINS.
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
Authors: Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Runbin Shi, Xue Lin, Yanzhi Wang
Date: 2020-09-16 00:00:00
Tasks: Deep Learning, Edge-computing, Image Denoising, Model Compression, Quantization

With the tremendous success of deep learning, there exists imminent need to deploy deep learning models onto edge devices. To tackle the limited computing and storage resources in edge devices, model compression techniques have been widely used to trim deep neural network (DNN) models for on-device inference execution. This paper targets the commonly used FPGA (field programmable gate array) devices as the hardware platforms for DNN edge computing. We focus on the DNN quantization as the main model compression technique, since DNN quantization has been of great importance for the implementations of DNN models on the hardware platforms. The novelty of this work comes in twofold: (i) We propose a mixed-scheme DNN quantization method that incorporates both the linear and non-linear number systems for quantization, with the aim to boost the utilization of the heterogeneous computing resources, i.e., LUTs (look up tables) and DSPs (digital signal processors) on an FPGA. Note that all the existing (single-scheme) quantization methods can only utilize one type of resources (either LUTs or DSPs for the MAC (multiply-accumulate) operations in deep learning computations. (ii) We use a quantization method that supports multiple precisions along the intra-layer dimension, while the existing quantization methods apply multi-precision quantization along the inter-layer dimension. The intra-layer multi-precision method can uniform the hardware configurations for different layers to reduce computation overhead and at the same time preserve the model accuracy as the inter-layer approach.