pwc-archive/papers-with-abstracts huggingface dataset:
ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification
Authors: Jinping Wang, Xiaojun Tan, JianHuang Lai, Jun Li, Canqun Xiang
Date: 2021-04-25 00:00:00
Tasks: General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Previous studies have shown the great potential of capsule networks for the spatial contextual feature extraction from {hyperspectral images (HSIs)}. However, the sampling locations of the convolutional kernels of capsules are fixed and cannot be adaptively changed according to the inconsistent semantic information of HSIs. Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field. Note that this unit can learn more discriminative representations of HSIs with fewer parameters. Specifically, two cascaded ASP-based convolution operations (ASPConvs) are applied to input images to learn relatively high-level semantic features, transmitting hierarchical structures among capsules more accurately than the use of the most fundamental features. Furthermore, the semantic features are fed into ASP-based conv-capsule operations (ASPCaps) to explore the shapes of objects among the capsules in an adaptive manner, further exploring the potential of capsule networks. Finally, the class labels of image patches centered on test samples can be determined according to the fully connected capsule layer. Experiments on three public datasets demonstrate that ASPCNet can yield competitive performance with higher accuracies than state-of-the-art methods.
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Authors: Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng
Date: 2021-04-25 00:00:00
Proceeding: ICLR 2022 4
Tasks: Representation Learning, Self-Supervised Learning

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained. Unfortunately, such a problem setting is often impractical if not infeasible since many real-world tasks rely on sequential learning, e.g., data are decentralized or collected in a streaming fashion. In this paper, we conduct the first thorough and dedicated investigation on self-supervised pre-training with streaming data, aiming to shed light on the model behavior under this overlooked setup. Specifically, we pre-train over 500 models on four categories of pre-training streaming data from ImageNet and DomainNet and evaluate them on three types of downstream tasks and 12 different downstream datasets. Our studies show that, somehow beyond our expectation, with simple data replay or parameter regularization, sequential self-supervised pre-training turns out to be an efficient alternative for joint pre-training, as the performances of the former are mostly on par with those of the latter. Moreover, catastrophic forgetting, a common issue in sequential supervised learning, is much alleviated in sequential self-supervised learning (SSL), which is well justified through our comprehensive empirical analysis on representations and the sharpness of minima in the loss landscape. Our findings, therefore, suggest that, in practice, for SSL, the cumbersome joint training can be replaced mainly by sequential learning, which in turn enables a much broader spectrum of potential application scenarios.
Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectors
Authors: Xinwei Zhao, Matthew C. Stamm
Date: 2021-04-25 00:00:00
Tasks: Misinformation

Visually realistic GAN-generated images have recently emerged as an important misinformation threat. Research has shown that these synthetic images contain forensic traces that are readily identifiable by forensic detectors. Unfortunately, these detectors are built upon neural networks, which are vulnerable to recently developed adversarial attacks. In this paper, we propose a new anti-forensic attack capable of fooling GAN-generated image detectors. Our attack uses an adversarially trained generator to synthesize traces that these detectors associate with real images. Furthermore, we propose a technique to train our attack so that it can achieve transferability, i.e. it can fool unknown CNNs that it was not explicitly trained against. We evaluate our attack through an extensive set of experiments, where we show that our attack can fool eight state-of-the-art detection CNNs with synthetic images created using seven different GANs, and outperform other alternative attacks.
Swimmer Stroke Rate Estimation From Overhead Race Video
Authors: Timothy Woinoski, Ivan V. Bajić
Date: 2021-04-25 00:00:00

In this work, we propose a swimming analytics system for automatically determining swimmer stroke rates from overhead race video (ORV). General ORV is defined as any footage of swimmers in competition, taken for the purposes of viewing or analysis. Examples of this are footage from live streams, broadcasts, or specialized camera equipment, with or without camera motion. These are the most typical forms of swimming competition footage. We detail how to create a system that will automatically collect swimmer stroke rates in any competition, given the video of the competition of interest. With this information, better systems can be created and additions to our analytics system can be proposed to automatically extract other swimming metrics of interest.
3D-TalkEmo: Learning to Synthesize 3D Emotional Talking Head
Authors: Qianyun Wang, Zhenfeng Fan, Shihong Xia
Date: 2021-04-25 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, Talking Face Generation, Talking Head Generation

Impressive progress has been made in audio-driven 3D facial animation recently, but synthesizing 3D talking-head with rich emotion is still unsolved. This is due to the lack of 3D generative models and available 3D emotional dataset with synchronized audios. To address this, we introduce 3D-TalkEmo, a deep neural network that generates 3D talking head animation with various emotions. We also create a large 3D dataset with synchronized audios and videos, rich corpus, as well as various emotion states of different persons with the sophisticated 3D face reconstruction methods. In the emotion generation network, we propose a novel 3D face representation structure - geometry map by classical multi-dimensional scaling analysis. It maps the coordinates of vertices on a 3D face to a canonical image plane, while preserving the vertex-to-vertex geodesic distance metric in a least-square sense. This maintains the adjacency relationship of each vertex and holds the effective convolutional structure for the 3D facial surface. Taking a neutral 3D mesh and a speech signal as inputs, the 3D-TalkEmo is able to generate vivid facial animations. Moreover, it provides access to change the emotion state of the animated speaker. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating the generated talking-heads of significantly higher quality compared to previous state-of-the-art methods.
Quantization of Deep Neural Networks for Accurate Edge Computing
Authors: Wentao Chen, Hailong Qiu, Jian Zhuang, Chutong Zhang, Yu Hu, Qing Lu, Tianchen Wang, Yiyu Shi, Meiping Huang, Xiaowe Xu
Date: 2021-04-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Edge-computing, Image Segmentation, Quantization, Semantic Segmentation, speech-recognition, Speech Recognition

Deep neural networks (DNNs) have demonstrated their great potential in recent years, exceeding the per-formance of human experts in a wide range of applications. Due to their large sizes, however, compressiontechniques such as weight quantization and pruning are usually applied before they can be accommodated onthe edge. It is generally believed that quantization leads to performance degradation, and plenty of existingworks have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue thatquantization, which essentially imposes regularization on weight representations, can sometimes help toimprove accuracy. We conduct comprehensive experiments on three widely used applications: fully con-nected network (FCN) for biomedical image segmentation, convolutional neural network (CNN) for imageclassification on ImageNet, and recurrent neural network (RNN) for automatic speech recognition, and experi-mental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applicationsrespectively with 3.5x-6.4x memory reduction.
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images
Authors: Xiaowe Xu, Jiawei Zhang, Jinglan Liu, Yukun Ding, Tianchen Wang, Hailong Qiu, Haiyun Yuan, Jian Zhuang, Wen Xie, Yuhao Dong, Qianjun Jia, Meiping Huang, Yiyu Shi
Date: 2021-04-25 00:00:00
Tasks: Computed Tomography (CT), Denoising, Image Denoising, Image-to-Image Translation, Translation

As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients. However, CT image quality is directly related to radiation dose, thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high dose like high-quality CT images (domain X) from low dose low-quality CTimages (domain Y), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). In this paper, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, while the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperformsCCADN in terms of denoising quality with slightly less computation resource consumption.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
Authors: Zikai Zhang, Bineng Zhong, Shengping Zhang, Zhenjun Tang, Xin Liu, Zhaoxiang Zhang
Date: 2021-04-25 00:00:00
Proceeding: CVPR 2021 1
Tasks: Multiple Object Tracking, Object Tracking, Philosophy

A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism. However, most state-of-the-art long-term trackers (e.g., Pseudo and re-detecting based ones) do not take all three key properties into account and therefore may either be time-consuming or drift to distractors. To address the issues, we propose a two-task tracking frame work (named DMTrack), which utilizes two core components (i.e., one-shot detection and re-identification (re-id) association) to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy. To achieve precise and fast global detection, we construct a lightweight one-shot detector using a novel dynamic convolutions generation method, which provides a unified and more flexible way for fusing target information into the search field. To distinguish the target from distractors, we resort to the philosophy of MOT to reason distractors explicitly by maintaining all potential similarities' tracklets. Benefited from the strength of high recall detection and explicit object association, our tracker achieves state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT2019LT benchmarks and runs in real-time (3x faster than comparisons).
Deep Convolutional Neural Network for Non-rigid Image Registration
Authors: Eduard F. Durech
Date: 2021-04-24 00:00:00
Tasks: Image Registration, Time Series, Time Series Analysis

Images taken at different times or positions undergo transformations such as rotation, scaling, skewing, and more. The process of aligning different images which have undergone transformations can be done via registration. Registration is desirable when analyzing time-series data for tracking, averaging, or differential diagnoses of diseases. Efficient registration methods exist for rigid (including linear or affine) transformations; however, for non-rigid (also known as non-affine) transformations, current methods are computationally expensive and time-consuming. In this report, I will explore the ability of a deep neural network (DNN) and, more specifically, a deep convolutional neural network (CNN) to efficiently perform non-rigid image registration. The experimental results show that a CNN can be used for efficient non-rigid image registration and in significantly less computational time than a conventional Diffeomorphic Demons or Pyramiding approach.
Calibrating LiDAR and Camera using Semantic Mutual information
Authors: Peng Jiang, Philip Osteen, Srikanth Saripalli
Date: 2021-04-24 00:00:00
Tasks: Image Registration

We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural network to estimate semantic mutual information, and matrix exponential for calibration computation. Using kernel-based sampling to sample data from camera measurement based on LiDAR projected points, we formulate the problem as a novel differentiable objective function which supports the use of gradient-based optimization methods. We also introduce an initial calibration method using 2D MI-based image registration. Finally, we demonstrate the robustness of our method and quantitatively analyze the accuracy on a synthetic dataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D benchmark datasets, showing improvement over recent comparable approaches.
Generalized moduli of continuity under irregular or random deformations via multiscale analysis
Authors: Fabio Nicola, S. Ivan Trapasso
Date: 2021-04-24 00:00:00

Motivated by the problem of robustness to deformations of the input for deep convolutional neural networks, we identify signal classes which are inherently stable to irregular deformations induced by distortion fields $\tau\in L^\infty(\mathbb{R}^d;\mathbb{R}^d)$, to be characterized in terms of a generalized modulus of continuity associated with the deformation operator. Resorting to ideas of harmonic and multiscale analysis, we prove that for signals in multiresolution approximation spaces $U_s$ at scale $s$, stability in $L^2$ holds in the regime $\|\tau\|_{L^\infty}/s\ll 1$ - essentially as an effect of the uncertainty principle. Instability occurs when $\|\tau\|_{L^\infty}/s\gg 1$, and we provide a sharp upper bound for the asymptotic growth rate. The stability results are then extended to signals in the Besov space $B^{d/2}_{2,1}$ tailored to the given multiresolution approximation. We also consider the case of more general time-frequency deformations. Finally, we provide stochastic versions of the aforementioned results, namely we study the issue of stability in mean when $\tau(x)$ is modeled as a random field (not bounded, in general) with identically distributed variables $|\tau(x)|$, $x\in\mathbb{R}^d$.
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning
Authors: Navid Ghassemi, Afshin Shoeibi, Marjane Khodatars, Jonathan Heras, Alireza Rahimi, Assef Zare, Ram Bilas Pachori, J. Manuel Gorriz
Date: 2021-04-24 00:00:00
Tasks: Computed Tomography (CT), Data Augmentation, Diagnostic, Transfer Learning

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly.
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
Authors: Mengyao Zhai, Lei Chen, JiaWei He, Megha Nawhal, Frederick Tung, Greg Mori
Date: 2021-04-24 00:00:00
Proceeding: ECCV 2020 8
Tasks: Lifelong learning

Humans accumulate knowledge in a lifelong fashion. Modern deep neural networks, on the other hand, are susceptible to catastrophic forgetting: when adapted to perform new tasks, they often fail to preserve their performance on previously learned tasks. Given a sequence of tasks, a naive approach addressing catastrophic forgetting is to train a separate standalone model for each task, which scales the total number of parameters drastically without efficiently utilizing previous models. In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks. For the current task, our model achieves high generation quality on par with a standalone model at a lower number of parameters. For previous tasks, our model can also preserve generation quality since the filters for previous tasks are not altered. We validate Piggyback GAN on various image-conditioned generation tasks across different domains, and provide qualitative and quantitative results to show that the proposed approach can address catastrophic forgetting effectively and efficiently.
Adaptive Appearance Rendering
Authors: Mengyao Zhai, Ruizhi Deng, Jiacheng Chen, Lei Chen, Zhiwei Deng, Greg Mori
Date: 2021-04-24 00:00:00
Tasks: Decoder, Video Generation

We propose an approach to generate images of people given a desired appearance and pose. Disentangled representations of pose and appearance are necessary to handle the compound variability in the resulting generated images. Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets' poses using an encoder-decoder neural network. Then the targets' appearances are encoded by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in the encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate images and videos that are superior to state-of-the-art methods, and can handle pose guided appearance rendering in both image and video generation.
Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image
Authors: Qi Wang, Yanling Miao, Mulin Chen, Xuelong Li
Date: 2021-04-24 00:00:00
Tasks: Clustering

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, have shown dominant performance. However, the high dimensionality of HSI data makes it hard to construct the pairwise adjacent graph. Besides, abundant spatial structures are often overlooked during the clustering procedure. In order to better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The SSCAG has the following contributions: 1) the anchor graph-based strategy is used to construct a tractable large graph for HSI data, which effectively exploits all data points and reduces the computational complexity; 2) a new similarity metric is presented to embed the spatial-spectral information into the combined adjacent graph, which can mine the intrinsic property structure of HSI data; 3) an effective neighbors assignment strategy is adopted in the optimization, which performs the singular value decomposition (SVD) on the adjacent graph to get solutions efficiently. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
Authors: Tianrui Guan, Jun Wang, Shiyi Lan, Rohan Chandra, Zuxuan Wu, Larry Davis, Dinesh Manocha
Date: 2021-04-24 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, Relation

We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids. M3DeTR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers. We perform extensive ablation experiments that highlight the benefits of fusing representation and scale, and modeling the relationships. Our method achieves state-of-the-art performance on the KITTI 3D object detection dataset and Waymo Open Dataset. Results show that M3DeTR improves the baseline significantly by 1.48% mAP for all classes on Waymo Open Dataset. In particular, our approach ranks 1st on the well-known KITTI 3D Detection Benchmark for both car and cyclist classes, and ranks 1st on Waymo Open Dataset with single frame point cloud input. Our code is available at: https://github.com/rayguan97/M3DETR.
A Survey of Modern Deep Learning based Object Detection Models
Authors: Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Asghar, Brian Lee
Date: 2021-04-24 00:00:00
Tasks: Classification, Deep Learning, General Classification, Object, object-detection, Object Detection, Survey

Object Detection is the task of classification and localization of objects in an image or video. It has gained prominence in recent years due to its widespread applications. This article surveys recent developments in deep learning based object detectors. Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks. It also covers contemporary lightweight classification models used on edge devices. Lastly, we compare the performances of these architectures on multiple metrics.