pwc-archive/papers-with-abstracts huggingface dataset:
Variational Autoencoders for Precoding Matrices with High Spectral Efficiency
Authors: Evgeny Bobrov, Alexander Markov, Sviatoslav Panchenko, Dmitry Vetrov
Date: 2021-11-23 00:00:00
Tasks: Management, Vocal Bursts Intensity Prediction

Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider the problem of finding precoding matrices with high spectral efficiency (SE) using variational autoencoder (VAE). We propose a computationally efficient algorithm for sampling precoding matrices with minimal loss of quality compared to the optimal precoding. In addition to VAE, we use the conditional variational autoencoder (CVAE) to build a unified generative model. Both of these methods are able to reconstruct the distribution of precoding matrices of high SE by sampling latent variables. This distribution obtained using VAE and CVAE methods is described in the literature for the first time.
Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data
Authors: Jun Ma, Huanfeng Shen, Penghai Wu, Jingan Wu, Meiling Gao, Chunlei Meng
Date: 2021-11-29 00:00:00

Land surface temperature (LST) is a key parameter when monitoring land surface processes. However, cloud contamination and the tradeoff between the spatial and temporal resolutions greatly impede the access to high-quality thermal infrared (TIR) remote sensing data. Despite the massive efforts made to solve these dilemmas, it is still difficult to generate LST estimates with concurrent spatial completeness and a high spatio-temporal resolution. Land surface models (LSMs) can be used to simulate gapless LST with a high temporal resolution, but this usually comes with a low spatial resolution. In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution. The global linear model (GloLM) model and the diurnal land surface temperature cycle (DTC) model are respectively performed as preprocessing steps for sensor and temporal normalization between the different LST data. The Landsat LST, Moderate Resolution Imaging Spectroradiometer (MODIS) LST, and Community Land Model Version 5.0 (CLM 5.0)-simulated LST are then fused using a filter-based spatio-temporal integrated fusion model. Evaluations were implemented in an urban-dominated region (the city of Wuhan in China) and a natural-dominated region (the Heihe River Basin in China), in terms of accuracy, spatial variability, and diurnal temporal dynamics. Results indicate that the fused LST is highly consistent with actual Landsat LST data (in situ LST measurements), in terms of a Pearson correlation coefficient of 0.94 (0.97-0.99), a mean absolute error of 0.71-0.98 K (0.82-3.17 K), and a root-mean-square error of 0.97-1.26 K (1.09-3.97 K).
OCR-free Document Understanding Transformer
Authors: Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park
Date: 2021-11-30 00:00:00
Tasks: Document Image Classification, document understanding, Key-value Pair Extraction, Optical Character Recognition, Optical Character Recognition (OCR), Visual Question Answering (VQA)

Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs. Although such OCR-based approaches have shown promising performance, they suffer from 1) high computational costs for using OCR; 2) inflexibility of OCR models on languages or types of document; 3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut, which stands for Document understanding transformer. As the first step in OCR-free VDU research, we propose a simple architecture (i.e., Transformer) with a pre-training objective (i.e., cross-entropy loss). Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model, Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy. In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains. The code, trained model and synthetic data are available at https://github.com/clovaai/donut.
Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction
Authors: Jiayi Pan, Heye Zhang, Weifei Wu, Zhifan Gao, Weiwen Wu
Date: 2021-11-28 00:00:00
Tasks: Image Reconstruction

Decreasing projection views to lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a Multi-domain Integrative Swin Transformer network (MIST-net) was developed in this article. First, MIST-net incorporated lavish domain features from data, residual-data, image, and residual-image using flexible network architectures, where residual-data and residual-image sub-network was considered as data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experiment results on numerical and real cardiac clinical datasets with 48-views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors.
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
Authors: Lukas Hoyer, Dengxin Dai, Luc van Gool
Date: 2021-11-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Domain Adaptation, Image-to-Image Translation, Semantic Segmentation, Synthetic-to-Real Translation, Unsupervised Domain Adaptation

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large number of methods propose new adaptation strategies, they are mostly based on outdated network architectures. As the influence of recent network architectures has not been systematically studied, we first benchmark different network architectures for UDA and newly reveal the potential of Transformers for UDA semantic segmentation. Based on the findings, we propose a novel UDA method, DAFormer. The network architecture of DAFormer consists of a Transformer encoder and a multi-level context-aware feature fusion decoder. It is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting to the source domain: While (1) Rare Class Sampling on the source domain improves the quality of the pseudo-labels by mitigating the confirmation bias of self-training toward common classes, (2) a Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote feature transfer from ImageNet pretraining. DAFormer represents a major advance in UDA. It improves the state of the art by 10.8 mIoU for GTA-to-Cityscapes and 5.4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes such as train, bus, and truck well. The implementation is available at https://github.com/lhoyer/DAFormer.
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Authors: Wei-Hong Li, Xialei Liu, Hakan Bilen
Date: 2021-11-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Multi-Task Learning, Prediction

Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data (i.e. not all the task labels are available for each image), which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
Authors: Guilherme Pombo, Robert Gray, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, John Ashburner, Parashkev Nachev
Date: 2021-11-29 00:00:00
Tasks: counterfactual

We describe Countersynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesized counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fr\'{e}chet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available online.
How Facial Features Convey Attention in Stationary Environments
Authors: Janelle Domantay
Date: 2021-11-29 00:00:00
Tasks: Deep Learning

Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNN's and CRNN's). Interestingly, CRNN's did not appear to perform significantly better than their CNN counterparts. While deep learning methods achieved greater prediction accuracy, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods.
Morph Detection Enhanced by Structured Group Sparsity
Authors: Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2021-11-29 00:00:00
Tasks: Face Recognition, feature selection, MORPH

In this paper, we consider the challenge of face morphing attacks, which substantially undermine the integrity of face recognition systems such as those adopted for use in border protection agencies. Morph detection can be formulated as extracting fine-grained representations, where local discriminative features are harnessed for learning a hypothesis. To acquire discriminative features at different granularity as well as a decoupled spectral information, we leverage wavelet domain analysis to gain insight into the spatial-frequency content of a morphed face. As such, instead of using images in the RGB domain, we decompose every image into its wavelet sub-bands using 2D wavelet decomposition and a deep supervised feature selection scheme is employed to find the most discriminative wavelet sub-bands of input images. To this end, we train a Deep Neural Network (DNN) morph detector using the decomposed wavelet sub-bands of the morphed and bona fide images. In the training phase, our structured group sparsity-constrained DNN picks the most discriminative wavelet sub-bands out of all the sub-bands, with which we retrain our DNN, resulting in a precise detection of morphed images when inference is achieved on a probe image. The efficacy of our deep morph detector which is enhanced by structured group lasso is validated through experiments on three facial morph image databases, i.e., VISAPP17, LMA, and MorGAN.
Image denoising by Super Neurons: Why go deep?
Authors: Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
Date: 2021-11-29 00:00:00
Tasks: Denoising, Image Denoising

Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep convolutional neural networks (CNNs) to effectively learn the mapping from noisy to clean images. Deep denoising CNNs manifest a high learning capacity and integrate non-local information owing to the large receptive field yielded by numerous cascade of hidden layers. However, deep networks are also computationally complex and require large data for training. To address these issues, this study draws the focus on the Self-organized Operational Neural Networks (Self-ONNs) empowered by a novel neuron model that can achieve a similar or better denoising performance with a compact and shallow model. Recently, the concept of super-neurons has been introduced which augment the non-linear transformations of generative neurons by utilizing non-localized kernel locations for an enhanced receptive field size. This is the key accomplishment which renders the need for a deep network configuration. As the integration of non-local information is known to benefit denoising, in this work we investigate the use of super neurons for both synthetic and real-world image denoising. We also discuss the practical issues in implementing the super neuron model on GPUs and propose a trade-off between the heterogeneity of non-localized operations and computational complexity. Our results demonstrate that with the same width and depth, Self-ONNs with super neurons provide a significant boost of denoising performance over the networks with generative and convolutional neurons for both denoising tasks. Moreover, results demonstrate that Self-ONNs with super neurons can achieve a competitive and superior synthetic denoising performances than well-known deep CNN denoisers for synthetic and real-world denoising, respectively.
Neural Attention for Image Captioning: Review of Outstanding Methods
Authors: Zanyar Zohourianshahzadi, Jugal K. Kalita
Date: 2021-11-29 00:00:00
Tasks: Decoder, Deep Learning, Image Captioning

Image captioning is the task of automatically generating sentences that describe an input image in the best way possible. The most successful techniques for automatically generating image captions have recently used attentive deep learning models. There are variations in the way deep learning models with attention are designed. In this survey, we provide a review of literature related to attentive deep learning models for image captioning. Instead of offering a comprehensive review of all prior work on deep image captioning models, we explain various types of attention mechanisms used for the task of image captioning in deep learning models. The most successful deep learning models used for image captioning follow the encoder-decoder architecture, although there are differences in the way these models employ attention mechanisms. Via analysis on performance results from different attentive deep models for image captioning, we aim at finding the most successful types of attention mechanisms in deep models for image captioning. Soft attention, bottom-up attention, and multi-head attention are the types of attention mechanism widely used in state-of-the-art attentive deep learning models for image captioning. At the current time, the best results are achieved from variants of multi-head attention with bottom-up attention.
Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
Authors: Songyang Zhang, Qinwen Deng, Zhi Ding
Date: 2021-11-29 00:00:00
Tasks: Clustering, Hyperspectral Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Hyperspectral imaging is an important sensing technology with broad applications and impact in areas including environmental science, weather, and geo/space exploration. One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. Leveraging on the recent-developed graph signal processing over multilayer networks (M-GSP), this work proposes several approaches to HSI segmentation based on M-GSP feature extraction. To capture joint spectral-spatial information, we first customize a tensor-based multilayer network (MLN) model for HSI, and define a MLN singular space for feature extraction. We then develop an unsupervised HSI segmentation method by utilizing MLN spectral clustering. Regrouping HSI pixels via MLN-based clustering, we further propose a semi-supervised HSI classification based on multi-resolution fusions of superpixels. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.
Adaptive Gating for Single-Photon 3D Imaging
Authors: Ryan Po, Adithya Pediredla, Ioannis Gkioulekas
Date: 2021-11-30 00:00:00
Proceeding: CVPR 2022 1
Tasks: Position, Thompson Sampling

Single-photon avalanche diodes (SPADs) are growing in popularity for depth sensing tasks. However, SPADs still struggle in the presence of high ambient light due to the effects of pile-up. Conventional techniques leverage fixed or asynchronous gating to minimize pile-up effects, but these gating schemes are all non-adaptive, as they are unable to incorporate factors such as scene priors and previous photon detections into their gating strategy. We propose an adaptive gating scheme built upon Thompson sampling. Adaptive gating periodically updates the gate position based on prior photon observations in order to minimize depth errors. Our experiments show that our gating strategy results in significantly reduced depth reconstruction error and acquisition time, even when operating outdoors under strong sunlight conditions.
AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant
Authors: Stan Weixian Lei, Difei Gao, Yuxuan Wang, Dongxing Mao, Zihan Liang, Lingmin Ran, Mike Zheng Shou
Date: 2021-11-30 00:00:00
Tasks: Question Answering, Retrieval, Video Question Answering, Video Retrieval

It is still a pipe dream that personal AI assistants on the phone and AR glasses can assist our daily life in addressing our questions like ``how to adjust the date for this watch?'' and ``how to set its heating duration? (while pointing at an oven)''. The queries used in conventional tasks (i.e. Video Question Answering, Video Retrieval, Moment Localization) are often factoid and based on pure text. In contrast, we present a new task called Task-oriented Question-driven Video Segment Retrieval (TQVSR). Each of our questions is an image-box-text query that focuses on affordance of items in our daily life and expects relevant answer segments to be retrieved from a corpus of instructional video-transcript segments. To support the study of this TQVSR task, we construct a new dataset called AssistSR. We design novel guidelines to create high-quality samples. This dataset contains 3.2k multimodal questions on 1.6k video segments from instructional videos on diverse daily-used items. To address TQVSR, we develop a simple yet effective model called Dual Multimodal Encoders (DME) that significantly outperforms several baseline methods while still having large room for improvement in the future. Moreover, we present detailed ablation analyses. Code and data are available at \url{https://github.com/StanLei52/TQVSR}.
Hole-robust Wireframe Detection
Authors: Naejin Kong, Kiwoong Park, Harshith Goka
Date: 2021-11-30 00:00:00

"Wireframe" is a line segment based representation designed to well capture large-scale visual properties of regular, structural shaped man-made scenes surrounding us. Unlike the wireframes, conventional edges or line segments focus on all visible edges and lines without particularly distinguishing which of them are more salient to man-made structural information. Existing wireframe detection models rely on supervising the annotated data but do not explicitly pay attention to understand how to compose the structural shapes of the scene. In addition, we often face that many foreground objects occluding the background scene interfere with proper inference of the full scene structure behind them. To resolve these problems, we first time in the field, propose new conditional data generation and training that help the model understand how to ignore occlusion indicated by holes, such as foreground object regions masked out on the image. In addition, we first time combine GAN in the model to let the model better predict underlying scene structure even beyond large holes. We also introduce pseudo labeling to further enlarge the model capacity to overcome small-scale labeled data. We show qualitatively and quantitatively that our approach significantly outperforms previous works unable to handle holes, as well as improves ordinary detection without holes given.
Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework
Authors: Lei Qi, Jiaqi Liu, Lei Wang, Yinghuan Shi, Xin Geng
Date: 2021-11-30 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Person Re-Identification, Unsupervised Domain Adaptation

Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. It aims to make a model trained on multiple source domains generalize to an unseen target domain. Although achieving promising progress, existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. In this paper, we turn to investigate unsupervised domain generalization for ReID, by assuming that no label is available for any source domains. To address this challenging setting, we propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module designed upon the batch and instance normalization techniques. In doing so, we successfully yield reliable pseudo-labels to implement training and also enhance the domain generalization capability of the model as required. In addition, we show that our framework can even be applied to improve person ReID under the settings of supervised domain generalization and unsupervised domain adaptation, demonstrating competitive performance with respect to relevant methods. Extensive experimental study on benchmark datasets is conducted to validate the proposed framework. A significance of our work lies in that it shows the potential of unsupervised domain generalization for person ReID and sets a strong baseline for the further research on this topic.